WO2024063678A1 - Determining a configuration of a wireless network - Google Patents

Abstract

Methods and apparatus are disclosed, including in an example a method of determining a configuration of a wireless communication network. The method comprises determining a state of the network, determining a model for predicting a result of applying a configuration to the network, and determining one or more objectives for providing one or more services using the network. The method also comprises determining, based on the state of the network and the model, a configuration for the network that, when applied to the network, satisfies one or more of the objectives.

Description

DETERMINING A CONFIGURATION OF A WIRELESS NETWORK

TECHNICAL FIELD

Example embodiments of this disclosure relate to determining a configuration of a wireless network.

BACKGROUND

Network life cycle management (LCM) is necessary to analyze, predict, fulfill and assure the requirements of different types of services for end-users using the network resources. An example of the context in which Network LCM is implemented is given in Figure 1. Current management approach or on-boarding a new customer requirement with 5G/beyond 5G is typically a lengthy process and involves phases shown in Figure 2, which shows an example of network life cycle management. The phases are as follows:

- Requirements gathering (6-12 weeks'): This is a manual process involving detailed discussions with the stakeholders to capture the functional and non-functional requirements.

- Planning (12 weeks'): Network planners use a diverse set of tools for traffic estimation, capacity budgeting, spectrum evaluation and site surveillance.

- Design & Simulation (12 weeks'): The plan is converted to high level design documents (cell location, spectrum, edge compute capacity) that may be implemented via low level orchestration (physical resource block partitions, Kubernetes pod placement).

- Fulfillment (>12-24 weeks): The design has to be implemented (preferably) over available sites. In case there are no suitable sites or carriers available, there would be further delays in timelines.

Tuning (12 weeks): Another lengthy process after fulfillment is tuning the network to meet realistic traffic patterns. Increased tuning may negate the plan and design produced in the preceding phases. - Assurance and Optimization: The assurance phase involves SLA compliance evaluation, QoS flow management, network slice lifecycle management. The assurance loop continuously monitors, configures and maintains the QoS performance levels as mandated by SLAs. This loop performs root cause analysis of potential problems and resolves them with minimal end-user impact, with the use of automation tools.

The various tasks in the phases are handled by individual teams within Communication Service Provider (CSPs), see “Driving 5G monetization through intent-based network

monetization-through-intent-based-network-operations. The tasks, data and knowledge artifacts, tools and processes can differ a lot among the phases. With the advent of Al, it is envisaged that many tasks within the phases would be solved using data-driven, ML-based algorithms.

There currently exist certain challenges. For example, the separation of concerns among the phases leads to easier management and independent evolution. However, there are drawbacks arising because of the phased architecture itself.

First, there is either no feedback from subsequent phases or when it exists, it is manual, sparse and incurs great delay. For example, feedback from the fulfilment phase could prompt a re-planning or re-designing the network before it is operational. In the absence of this feedback, a re-planning or re-design of the network triggered during the assurance phase will lead to disruption/degradation of deployed services and possible violation of SLAs. It is thus necessary to define and implement inter-phase closed loops.

Second, the architecture is not suited to admitting new requirements on-demand and handling drift in traffic patterns which would require continuous re-planning, design, tuning and assurance.

Third, the phased architecture is not agile enough to factor in the technology evolutions such as automation capabilities, virtualization and interacting closed loop control, that can greatly enhance network performance and management. There is a qualitative difference between the sequential and functional agents. Since the sequential agents necessarily construct a model of the environment (either implicit or explicit), their proposals are not greedy, i.e. an intermediate action proposal from a sequential agent may result in lower KPI values than functional agents because the sequential agents are able to predict that in the long term such a solution is going to give higher rewards. SUMMARY

Certain aspects of this disclosure and their embodiments may provide solutions to these or other challenges. For example, a cognitive framework based LCM as proposed herein according to example embodiments may solve one or more of the challenges of:

1 . fast feedback among the different phases,

2. continuous response, and

3. adaptation to technology evolution.

One example aspect of this disclosure provides a method of determining a configuration of a wireless communication network. The method comprises determining a state of the network, determining a model for predicting a result of applying a configuration to the network, and determining one or more objectives for providing one or more services using the network. The method also comprises determining, based on the state of the network and the model, a configuration for the network that, when applied to the network, satisfies one or more of the objectives.

Another example aspect of this disclosure provides apparatus for determining a configuration of a wireless communication network. The apparatus comprises a processor and a memory. The memory contains instructions executable by the processor such that the apparatus is operable to determine a state of the network, determine a model for predicting a result of applying a configuration to the network, determine one or more objectives for providing one or more services using the network, and determining, based on the state of the network and the model, a configuration for the network that, when applied to the network, satisfies one or more of the objectives.

A further example aspect of this disclosure provides apparatus for determining a configuration of a wireless communication network. The apparatus is configured to determine a state of the network, determine a model for predicting a result of applying a configuration to the network, determine one or more objectives for providing one or more services using the network, and determine, based on the state of the network and the model, a configuration for the network that, when applied to the network, satisfies one or more of the objectives. BRIEF DESCRIPTION OF THE FIGURES

For a better understanding of the embodiments of the present disclosure, and to show how it may be put into effect, reference will now be made, by way of example only, to the accompanying drawings, in which:

Figure 1 shows an example of the context in which Network LCM is implemented;

Figure 2 shows an example of Network Lifecycle Management;

Figure 3 shows an example of a cognitive framework;

Figure 4 shows an example of network LCM agent interaction with a cognitive framework;

Figures 5A and 5B show another example of network LCM agent interaction with a cognitive framework;

Figure 6 shows an example of an intent;

Figure 7 shows an example of a generated issue;

Figure 8 shows an example of a system state;

Figure 9 shows an example of an implementation of a forecasting agent;

Figure 10 shows an example of an output of a forecasting agent;

Figure 11 illustrates actions performed by an example forecasting agent;

Figure 12 shows an example of a planning agent;

Figure 13 shows an example of rules to generate average throughput per user;

Figure 14 shows an example of a formula to estimate the number of 5G PRBs to meet a throughput requirement;

Figure 15 shows an example of a design agent;

Figure 16 shows an example design output for a cell;

Figure 17 shows an intent view of a design agent;

Figure 18 shows an example of prior intents and/or flows fulfilled by current slices in a network; Figure 19 shows an example of PRB allocation requiring changes;

Figure 20 shows an example of PRB modified to satisfy new intent;

Figure 21 shows an example of an adaptive policy engine;

Figure 22 shows an example of a tuning agent;

Figure 23 shows an example of an assurance-optimization agent;

Figure 24 is a flow chart illustrating a method in accordance with some embodiments;

Figure 25 shows an example of how an intent-handling function works;

Figure 26 shows an example of how intent-handling functions can be combined to realize a complete intent-driven operations system;

Figure 27 shows an example of a cognitive layer between business operations and network/environment;

Figure 28 shows an example of a communication system in accordance with some embodiments;

Figure 29 shows a UE in accordance with some embodiments;

Figure 30 shows a network node in accordance with some embodiments;

Figure 31 is a block diagram of a host;

Figure 32 is a block diagram illustrating a virtualization environment in which functions implemented by some embodiments may be virtualized; and

Figure 33 shows a communication diagram of a host communicating via a network node with a UE over a partially wireless connection in accordance with some embodiments.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art.

An example cognitive framework (see for example “Cognitive processes for adaptive intentbased

review/articles/adaptive-intent-based-networkinq) consists of three components: a knowledge base, a reasoning engine and an agent architecture. As shown in Figure 3, which shows an example of a cognitive framework, the knowledge base contains the ontology of intents along with domain specific knowledge such as the current state of the system. The domain-independent reasoning engine serves as the central coordinator function and uses the knowledge graph which is a common data structure. The reasoning engine orchestrates a number of registered agents for finding solution actions, evaluating their impact and ordering their execution. Finally, the agent architecture allows any number of models and services to be used. Agents (also referred to herein in some examples as software components or software modules) can contain machine-learned models or rulebased policies, or implement services needed in the cognitive reasoning process.

Embodiments of this disclosure may implement one or more of the following features:

(1) Implementing network life cycle management (LCM) phases as a set of software modules, referred to herein in some examples as agents or intelligent agents, in the cognitive framework

(2) Model quality specification is specified as objectives (e.g. intents), along with network intents

(3) Identifying the data and grounding required to orchestrate the phase-agents

(4) Rules to process the knowledge of requirements and change in the resource layer state and assist orchestration of agents

(5) Designate all tasks within existing phases as a uniform pool, thus invoking the tasks in a finer granularity.

In some examples, the cognitive approach may be applied in a consistent way for the different LCM steps. Multiple agents for network LCM phases may be registered and use common knowledge from the cognitive layer. Rules in the cognitive framework may help in orchestrating these agents.

Cognitive Solution for Network LCM

A cognitive framework allows many possible ways of interacting with the external context (service and infrastructure layers) as well as defining and orchestrating the loops. An example approach is proposed where phases of the network LCM are modelled as agents. In some examples, there are the following agents: forecasting, planning-design (modelling planning and design phases), deployment (modelling fulfilment and tuning phases), and assurance-optimization. The Cognitive Framework orchestrates these phases, driven by intents and events, as shown in Figure 4, which shows an example of network LCM agent interaction with a cognitive framework.

Example embodiments of this disclosure may include one or more of the following steps:

1 . The objectives (intents) include (a) network intents coming from the service layer, and (2) intents specifying the required quality of models, for example, specifying a minimum desired accuracy of prediction.

2. CC processes the intents and invokes the Forecasting agent’s rules to populate state vectors from grounding agents (e.g. with machine learning (ML) based predictive model). The state vectors include network Key Performance Indicators (KPI’s) as well as model quality KPI’s.

3. The current and forecast state vectors are used to a. Generate issues if the intents (primary/secondary) intents are violated and corresponding goals generated.

Network issue

4. Assurance-optimization and Planning-design agents are invoked to propose solutions

5. If the current resources are sufficient to assure (satisfy) the intents with reconfiguration of the network, assurance agent returns the configuration.

6. Simultaneously, or subsequent to a negative report from the assurance agent, a planning-design agent is invoked: a. The planning function produces required capacity and priority of se rvi ces/cu sto m e rs . b. The design function is invoked to generate high- and then low-level designs. The low-level design is the solution proposal produced by the planning-design agent.

7. Proposals/Solutions (e.g. example or candidate network configurations) are augmented with certain cost estimating the penalties for the issues, which can be used to evaluate the quality of proposals and recommend the final proposal. Note: For example, certain reconfiguration proposals from the assurance-optimization agent may lead to degradation of service for low priority customers, however, repeated degradations may result in large long-term penalty in terms of loss of customers. In the latter case, a replanning proposal by planning-design agent would be recommended.

8. The fulfillment+tuning functions are part of the deployment agent to implement the design. a. Fulfillment function is the implementation of the design. b. The tuning function is invoked to ensure the sanity and readiness of the reconfigured network through controlled traffic. c. At the end, deployment agent lets the network go “live” with real, live traffic.

9. The loop repeats at 3 to keep monitoring and seeing if any issues are resolved. In case of resolution, it is known that the new intents (along with the existing ones) are satisfied by either the existing network via reconfiguration or by addition and configuration of new resources in the network.

Model issue

10. Specific rule(s) in the forecasting agent are invoked to suggest retraining/reselection of models. It is expected that any issue of low accuracy would be resolved.

Figures 5A and 5B shows another example of network LCM agent interaction with cognitive framework.

Intents

Intents (an example of or otherwise referred to herein as objectives) are, in some examples, formal representations of requirements and constraints. There can be different ways of specifying syntax and semantics of intent metamodels (for example, refer to TM-Forum autonomous networks). Figure 6 shows an example of an intent, which has an average theoretic throughput intent for eMBB downlink (DL) set to 500 (e.g. Gbps, Mbps, kbps etc).

If the intent is not satisfied, issues and goals may be generated by the cognitive framework. Figure 6 shows an example of a generated issue, where the target intent value is 500 as in Figures 5A and 5B, whereby the observed average throughput is 150. In some examples, system state (e.g. the state or current state of the network) is represented as a set of RDF triples <subject, predicate, object>. These are derived from various counters via grounding agents and can refer to actual, derived and forecast values. An example of the system state (or network state/current state) with a cell ID, PRB allocation and carrier specifications in the knowledge base is shown in Figure 8.

Forecast phase agent

Metadata about forecast models (machine learning or machine reasoning based) may be represented in some examples as knowledge in the knowledge base (KB). Agent application programming interfaces (APIs) may include one or more of the following:

1. registration,

2. measurement of various KPI’s as required by the intents,

3. comparison with previous prediction,

4. generate new metrics,

5. compare with base metrics,

6. trigger training with given dataset.

Example

A forecasting agent is registered to the cognitive layer and provides regression forecasts on the active user, throughput and traffic growth over multiple time horizons (weeks I months). Figure 9 shows an example of an implementation of a forecasting agent, and Figure 10 shows an example of an output of a forecasting agent.

Example cognitive framework artifacts, knowledge and rules may include one or more of the following:

1 . Model -> id, type, feature names and types, output names and types, pointer to reference test data, metrics (e.g. accuracy w.r.t the reference test data)

2. Rules to trigger measurement periodically or event-triggered manner, store in the KB (or external DB) and compare with previous prediction for fidelity check.

3. Rules to trigger fresh training based on discrepancy with reference metrics. In order to forecast the requirements of the 5G radio network (RAN, transport, core), the forecasting agent may consider multiple factors. These may include for example specification of the service, (trend) distribution of users, growth models, coverage of service (nation-wide, region, clusters).

As most operators have already deployed sites, one example strategy would be to reuse datasets from an existing mobile network (e.g. month-on-month growth in user traffic, observed throughput). This is then used to estimate the forecasted growth over a horizon (e.g. 24 month period). As the traffic only consists of eMBB devices in 4G, the inorganic growth of new service traffic can be estimated via MNO business plans for service deployment.

Figure 11 illustrates actions performed by an example forecasting agent. Given an intent to estimate the traffic growth over a horizon, it provides a model for eMBB and service level agreement (SLA) user traffic growth.

Figure 12 shows an example of a planning agent (or planning phase agent). Given the traffic forecasted traffic, the planning phase determines the additional capacity needed at the RAN, transport and core subnets. Figure 7 provides an overview of the planning agent. Note that this planning process makes use of multi-objective requirements including service KPIs, resource efficiency, cost, energy and revenue models.

Agent API may in some example include one or more of the following:

1 . Registration

2. Traffic forecast per service(existing, new)

3. Performance prediction (health check of existing services, new services)

4. Capacity calculation(existing, new)

5. Generate resource and service priorities

Related example cognitive frameworks artifacts may include one or more of the following:

1 . Services, their requirements and constraints (specified using intents)

2. Service and customer priorities

3. Resource budgets 4. CAPEX/OPEX estimates

5. Rules to trigger the API’s whenever inputs for the API’s change, periodically, or at user-configured events.

The API’s can be implemented using appropriate algorithms, which could be third-party in some examples. Rules may in some examples be implemented in the cognitive layer to generate average throughput observed by a set of users of a particular service/cell. Figure 13 shows an example of rules to generate average throughput per user.

Given a throughput requirement, the planning agent can in some examples make use of Radio Access Network (RAN) physical resource block (PRB) requirements to derive the additional radio resource capacity needed in the system. Figure 14 shows an example of a formula to estimate the number of 5G PRBs to meet a throughput requirement.

Desiqn phase

Examples of this process involve a high level design (if the available resources at RAN, transport and core can meet resources) and low-level design (host level information used to configure compute communication nodes). Figure 15 shows an example of a design agent and illustrates design agent functionalities. For instance, in case of RAN design, there are multiple possible design outputs:

• If there are sufficient PRBs, re-partition and allocate resources.

• Else, perform intra/inter-band carrier aggregation or load balancing to add additional PRBs.

• Else, add an additional antenna sector.

• Else, request for an additional cell site.

Note that some of these operations are lengthy (new cell site deployments) and must be avoided in case of alternatives.

An example design output for a cell NE20605 which proposes carrier aggregation and then partitioning is provided in Figure 16.

Similar granular plans may be done for the transport subnet (e.g. links, priority) and core subnet (e.g. scaling, workload migration). Given pre-deployed services (and associate network slices), the design phase may in some examples allocate the deployment of new services without significant impact (penalties, cost) on existing services. Figure 17 shows an example of an intent view of a design agent. That is, for example, Figure 17 provides the intent handling view of the design agent. Given the intent requirement of a service and the current resource capacity, the agent can in some examples, allocate resources with one of the following outcomes for the intent: (i) no-side effects (ii) need for reconfiguration of system (iii) impact on other intents (need for prioritization). Under extreme cases, this phase of the lifecycle can also trigger a re-planning of the deployment.

Example TOSCA-D and TOSCA-O generation

Given high level intent requirements, a planning agent may be called to estimate additional capacity needed by the system. Note that this view has only the aggregated capacity in the system. Figure 18 shows an example of prior intents and/or flows fulfilled by current slices in a network. Plans may be generated that could have an impact on pre-existing intents in the system (this can also be computed in the cognitive layer as part of the conflict handling loop in some examples). Topology and Orchestration Specification for Cloud Applications (TOSCA)-D plans may be input to the next set of problem files to generate TOSCA-O files in some examples. In an example, is level would have TOSCA-D and high level YANG models for RAN as follows: node_name : "central_office_l" nodelD : 1 processing : 6400 cores memory : 12800 GB storage : 10000 TB processing_price : 4 €/core/h memory_price : 0.2 €/GB/h storage_price : 0.03 €/TB/h availability: 0.9998 location: "Stockholm" features : [ "GPU-farm", "ECO-5" ] leaf nRPCI { description "The Physical Cell Identity (PCI) of the NR cell."; reference "3GPP TS 36.211"; mandatory true; type int32 { range "0..1007"; }

} leaf arfcnDL { description "NR Absolute Radio Frequency Channel Number (NR-ARFCN) for downlink."; reference "3GPP TS 38.104"; mandatory true; type int32;

} leaf arfcnUL { description "NR Absolute Radio Frequency Channel Number (NR-ARFCN) for uplink."; reference "3GPP TS 38.104"; type int32;

}

The generated TOSCA-D models may be moved in some examples to a fine-grained TOSCA-O model to move to configuration changes (PRB partition changes). Figure 19 shows an example of PRB allocation requiring changes, and Figure 20 shows an example of PRB modified to satisfy new intent (e.g. by re-engineering/configuring current slices).

Examples of TOSCA-O and low level YANG models to achieve this are as follows: node_name : "central_office_l" nodelD : 1 pods : 8 pod usage: 0.95 latency : 0.5 ms throughput : 40 Gb/s leaf ssbFrequency { description "Indicates cell defining SSB frequency domain position.

Frequency (in terms of NR-ARFCN) of the cell defining SSB transmission.

The frequency identifies the position of resource element RE=#0

(subcarrier #0) of resource block RB#10 of the SS block. The frequency must be positioned on the NR global frequency raster, as defined in 3GPP TS 38.101-1, and within bSChannelBwDL."; mandatory true; type int32 { range "0..3279165"; }

} leaf ssbPeriodicity { description "Indicates cell defined SSB periodicity. The SSB periodicity is used for the rate matching purpose."; mandatory true; type int32 { range units "subframes (

} leaf ssbSubCarrierSpacing { description "Subcarrier spacing of SSB. Only the values 15 kHz or 30 kHz

(< 6 GHz), 120 kHz or 240 kHz (> 6 GHz) are applicable."; reference "3GPP TS 38.211"; mandatory true; type int32 { range "15 | 30 | 120 | 240"; } units kHz;

}

While TOSCA-D/O and YANG models are separated into multiple design phases in some examples, these may be implemented in a single phase in other examples.

Deployment Phase agent - Fulfillment function

In some examples, the design output (e.g. network configuration) may be deployed by fulfillment agents to meet the 5G network and service requirements. There may be shorter horizon fulfillment task (performing carrier aggregation) or longer horizon tasks (adding a new cell site). The fulfilment phase gives feedback to the design phase.

The TOSCA-O would in some examples interface with the adaptive policy execution engine (APEX) to fulfill the generated design. The fulfilment agent may monitor the state of the system and can escalate deviations in design fulfilment.

Figure 21 shows an example of an adaptive policy engine, which takes input (e.g. stimulus event, trigger) from triggering system and provides output (e.g. response event, actions) to actioning system.

Figure 22 shows an example of a tuning agent. The tuning agent can in some examples interface between fulfillment and assurance by monitoring performance over live traffic and optimally configuring the subnets. Note that the intents at this phase could be “test intents” used to monitor efficacy of the fulfillment. One example technique to be considered would be reinforcement learning, wherein training could be done over the tuning period to suggest optimal configurations. In case the tuning phase does not yield sufficiently optimal outputs, this can trigger a re-design or re-plan of the deployment. The tuning phase can also in some examples provide feedback on the traffic mix assumptions (eMBB vs. SLA) that were used in the traffic forecasting and planning phases. Assurance-Optimization Phase agent

Figure 23 shows an example of an assurance-optimization agent. Assurance of QoS flow performance to various tiers of customers may in some examples involve managing the network slice lifecycle (creation, scaling and deletion), optimal prioritization and resource allocation, QoS deviation monitoring and intelligent action execution. In this case, the example agent shown in Figure 23 may have a view of the slices deployed as well as granular resource configuration at each sub-net. For instance, it can modify PRB partitions, change transport router priority or change affinity rules in Kubernetes pods. The assurance loop can in some examples make use of API calls to re-partition PRBs or migrate pods in Kubernetes.

Note that multiple objectives such as prioritize higher tiers of customers, perform actions with lowest side-effects (time/cost) and maximize resource usage may be input into the system in some examples. An advantage of interfacing with the cognitive framework in some examples may be that it can determine if an intent can be handled by the assurance/optimization agent (with configuration changes) or if it requires a coarser redesign or re-planning.

Alternative embodiments

While the agents have been sub-divided to map to specific phases to aid in knowledge management, agent lifecycle handling and scale, in some examples, moving forward to beyond 5G, all processing may be handled via a single end-to-end agent pipeline that receives information from the various phases. The best reuse of underlying knowledge and agents may in some examples be when the phase-agent boundaries are broken and all the resources are available to the cognitive framework. For example, the same planning or constraint solving agent can provide solution proposals for different tasks handled in different phases. New challenges such as the scalability of the cognitive framework may surface and may be tackled by distribution in some examples. In addition, increased automation and virtualization capabilities could in some examples eliminate one or more phases and/or merge multiple phases in the lifecycle. The agents may in some examples themselves be implemented in independent cognitive-based architectures. This may lead to not having to define loops explicitly.

Figure 24 depicts a method 2400 in accordance with particular embodiments, such as for example a method of determining a configuration of a wireless communication network. The method 2400 may be performed by a network node (e.g. the network node QQ110 or network node QQ300 as described later with reference to Figures 28 and 30 respectively). The method begins at step 2402 with determining a state of the network, and step 2404 with determining a model for predicting a result of applying a configuration to the network. Next, step 2406 comprises determining one or more objectives for providing one or more services using the network; and, and step 2408 comprises determining, based on the state of the network and the model, a configuration for the network that, when applied to the network, satisfies one or more of the objectives.

The following describes additional example embodiments.

5G networks introduce unprecedented flexibility and dynamic adaptation into service delivery and network resource utilization. In the business layer, this is reflected in the ability to offer customizable service products with detailed agreements on functional and non-functional characteristics as well as fast delivery. Dynamic adaptation to changes within the constraints of stringent requirements on lead and reaction times is beyond the capacity of a human workforce. Extensive automation may be necessary to overcome this challenge.

The zero-touch paradigm implies that the operation of services and the underlying networks is autonomous and does not require human intervention. To achieve this, the zero-touch system may be able to handle the complexity caused by continuous changes to the system at the same time that it delivers services to users and manages issues such as the cost of resources versus the budget available, the legal compliance of the service and the security of the setup. This is challenging for technical systems in real-world scenarios.

For example, successful service operation requires each service to be properly provisioned and assured to deliver the promised function with agreed performance metrics. Complexity arises as a result of changes to contracts, products, customer preferences, the business strategy or the environment in which the service is being offered. Users may start exhibiting new behavior, leading to varying service usage patterns and network loads, or the network may change due to upgrades, reconfiguration or outages. Some changes may be regular and predictable, whereas others are sudden and surprising.

To manage all of these concerns autonomously and adapt its behavior appropriately, a zerotouch system must understand every aspect of what is expected of it. Each requirement and goal must be carefully defined in order for technical processes to derive suitable and optimized actions to manage it. These definitions are known as intents (or more generally as objectives in some examples of this disclosure). From the perspective of a human operator, an intent expresses the expectation of what the operational system is supposed to deliver and how it behaves. In light of this, we define intent as “formal specification of all expectations including requirements, goals and constraints given to a technical system.”

The role of intents in cognitive networks

In some examples, everything an autonomous system needs to know about its goals and expected behavior may be defined with intents. The system will not perform any operation unless it relates to the fulfilment and assurance of an intent, which means that all goals - including those that may have been considered “common sense” in human-operated systems - may be expressed as intents.

An intent in an autonomous system is ideally expressed declaratively - that is, as a utilitylevel goal that describes the properties of a satisfactory outcome rather than prescribing a specific solution. This gives the system the flexibility to explore various solution options and find the optimal one. It also allows the system to optimize by choosing its own goals that maximize utility.

Unlike traditional software systems, where requirements are analyzed offline to detect and resolve conflicts prior to implementation, intents are added to an autonomous system during runtime. Adaptation to changed intent as well as conflict detection and resolution are therefore essential capabilities of an autonomous system.

One of the benefits of expressing intents as utility level goals is that it helps the system cope with the conflicting objectives of multiple intents. This is advantageous because an autonomous system often has to take multiple intents into account before making a decision.

For example, an autonomous system may have one intent to deliver a service with high QoE, while another may be to minimize resource spending. It can resolve such conflicts either explicitly from weights that introduce relative importance or implicitly from properties of preferential outcomes as defined in utility-level goals.

Expectations originate from contracts or business strategy and remain constant when the underlying system is replaced or modified. Consequently, when setting up the intents, it is important that they are formulated in an infrastructure-agnostic way, so that they can be transferred across system generations and implementations. In short, the intent establishes a universal mechanism for defining expectations for different layers of network operation. It expresses goals, utility, requirements and constraints. It defines expectations on service delivery as well as the behavior of the autonomous operational system and the underlying network.

Service-specific intents

In some examples, one type of intent relates to the specification of services. Service-specific intents state expected functional and performance characteristics. Service Level Agreements (SLAs), Service Level Specifications (SLSs), Service Level Objectives (SLOs) and TOSCA (Topology and Orchestration Specification for Cloud Applications) models are all examples of service-specific intents that are used on different levels in the operations stack.

SLAs are business support systems (BSS) objects. Service-specific intents based on SLAs specify the promised service and include expected performance details and business consequences such as payment for delivery and penalties when failing.

SLSs/SLOs define the service delivery details at operations support systems (OSS) level. Based on this input, autonomous OSS would plan detailed tasks to realize the service delivery. TOSCA models would be used to express further technical details the OSS generate (expectations from orchestration and assurance).

In multiple stages, the autonomous operation makes decisions about further details. Higher- level intent is the input leading to the lower-level intent that is used to distribute specific goals to subsystems. For example, SLAs/SLSs are the intents that express a terminal goal of the OSS. The OSS then decide which TOSCA model would be the best option to deliver the promised performance with minimal resource usage. The selected TOSCA model is the instrumental goal of OSS and becomes an intent and terminal goal for the orchestrator.

This pattern of making decisions based on a given intent and taking action by sending lower- level intents to subsystems is the key interwork mechanism of example intent-based operation, according to which the entire operations stack of autonomous networks is built.

Strategic and behavioral intents

Beyond all the service-specific intents that an autonomous system must have, it may also in some examples require guidance on how to handle strategic and behavioral concerns.

Traditionally implemented in the form of manually coded policies, this type of guidance steers general system behavior and supports the type of decision-making that has traditionally been based on human intuition and experience, along with knowledge about context and operator strategy. Intent-based operation makes it possible for operators that want to handle these concerns in a more dynamic fashion to replace manually coded policies with strategic and behavioral intents.

This may be useful for example in cases where the operator chooses to require a default minimum security level that differs from that which is implemented into the service, for example. In these cases, dedicated intent can be used to set the security level for all services that do not specify it directly.

With regard to legal compliance, services may be delivered in multiple markets where different rules apply. A legal-compliance intent requires compliance and potentially specifies the details.

Since there may be a risk of service degradation when changes are initiated and risky actions may sometimes lead to a higher margin, risk-management intents can be used in some examples to convey how the operator wants the autonomous system to balance risks versus potential gain.

Reporting/escalation intents steer how the autonomous system interacts with the human workforce by reporting progress status on intent fulfillment and seeking manual decision in escalations.

Formally expressing an intent

An autonomous system may in some examples intents to be formally defined in a machine- readable and processable way, but the broad range of considerations involved and their abstract semantics are often difficult to structure. Techniques from knowledge management and semantic modeling enable the creation of an ontology of intent, based on an extensible metamodel. Resource Description Framework (RDF) and RDF Schema standards can be used for knowledge modeling.

Technical functions such as contract and order management could in some examples be used to directly use RDF objects to communicate intent. Intent specified directly by human operators would require an intuitive frontend, potentially using natural language.

Intent handling

The operation of services within an intent-based network may also in some examples require the introduction of intent handling functions in the operations stack and functional architecture. An intent-handling function receives the intents, decides which actions must be taken to optimally fulfill all given intents and implements its decisions.

Intent-handling functions may have a knowledge base that contains the intent ontology. They may also have machine-reasoning capabilities to realize knowledge-driven decision-making processes.

Machine reasoning plays a key role in intent handling in some examples, with its capability to understand abstract concepts from diverse domains and provide precise, specialized conclusions based on precedent and observation. Probabilistic modeling contributes quantification of risk and uncertainty, which is essential to make informed decisions when facing conflicting goals and new situations.

Figure 25 shows an example of how the intent-handling function works. While its implementation is domain-specific, its interface is generic. It receives intents that express all types of expectations. It is equipped with policies and artificial intelligence (Al) models that implement the capabilities needed for analyzing the system state and finding optimized operational actions based on observations from the operated environment. The intent handler also reports the fulfillment and assurance status of its intents.

The API (application programming interface) of the intent-handling function is domainindependent in some examples. Its main objective is to manage the life cycle of intents. It implements methods to set, modify and remove intents and send reports. Intent is constructed based on a common intent meta-model and its details are specified according to domain-specific information models. Intent management is therefore primarily knowledge management.

Figure 26 shows an example of how intent-handling functions can be combined to realize a complete intent-driven operations system. Every major system layer and subsystem domain, including BSS, OSS, orchestration and network management, contains an intenthandling function. Intent originates in some examples from functions such as contract and order management. Additional intents can be entered directly through portals.

Introducing the cognitive layer

Lexico defines cognition as: “... the mental action or process of acquiring knowledge and understanding through experience and the senses.” As it is designed to perform the equivalent operational tasks of understanding through experiencing and sensing, an autonomous system may in some examples be, therefore, a technical implementation of cognition.

Creating an intent-handling function that understands complex and abstract intent semantics, derives the optimal target state and plans actions for transitioning the system into this state is a challenging task. The function may in some examples be able to explore options, learn from precedents and assess the feasibility of actions based on their expected consequences.

By combining well-understood Al techniques within a flexible architecture, we have designed a cognitive system that specializes in autonomous service and network operation. We refer to it as the cognitive layer, and its role is to serve as an interface between business operations and the network/environment, as shown in Figure 27, which shows an example of a cognitive layer between business operations and network/environment.

The cognitive layer consists of three components in this example: a knowledge base, a reasoning engine and an agent architecture. The knowledge base contains the ontology of intents along with domain-specific knowledge such as the current state of the system. The domain-independent reasoning engine uses the knowledge graph and serves as the central coordinator function for finding actions, evaluating their impact and ordering their execution. Finally, the agent architecture allows any number of models and services to be used. Agents can contain machine-learned models or rule-based policies, or implement services needed in the cognitive reasoning process.

To be usable, in some examples an agent may be registered and described in the knowledge base. Its description can be added and modified at any time, allowing life cycles of the models, policies and supplementary services to be decoupled from the overall life cycle of the cognitive layer.

The agent metadata contains for example a description of the agent interface, along with its function, role and capabilities. For example, we have implemented a machine-learned model that can propose radio base station configurations that optimize the service experience. This model is registered as an agent in the role of a “proposer” for configuration actions.

The separate life cycle makes it possible for the model to be replaced with an improved version when available, independent of the cognitive layer release cycles.

We have also demonstrated agents in the role of “predictor” with the ability to estimate the effect of actions on key performance indicators (KPIs). An agent in an “observer” role would monitor data sources, keeping knowledge about the state up-to-date. An agent in the “actuator” role can implement actions in the network by utilizing, for example, established network management functions.

How the cognitive layer works

The successful operation of the cognitive layer may in some examples depend on smooth interaction between the reasoning engine and the knowledge base. For example, the reasoning engine continuously executes a process that tries to find actions to close the gap between the current observed state and the wanted state, according to the intent. It collects proposals, obtains predictions on the effect of each proposal, evaluates gain versus risk and certainty, prioritizes actions and executes its decisions. Specialist agents are used intensively in every step of the process.

The reasoning engine may in some examples be an adaptive knowledge-driven composer that can instantiate the cognitive process following changes in intent, state and context. It can dynamically compose specialized agents and add them into the process if their capabilities and roles match the needs according to intent and context. This is, for example, how the cognitive layer obtains action proposals from agents that are implementing suitable models.

In examples where the capabilities of multiple agents match the requirements of a role, each of them may for example be used simultaneously to generate alternative solution strategies (e.g. candidate network configurations), resulting in a diverse set of options for the prediction and evaluation steps that follow. This coexistence of agents makes it possible to combine rule- and policy-based implementations with machine-learned alternatives in the same system, enabling the system to acquire new advanced abilities without losing current ones.

In some examples, the cognitive process is a perpetual loop that starts again directly after the previous iteration has finished. Any degradation of the network or issues with services would be visible in the observed state. By trying to close the gap to the wanted state set by intent, the cognitive layer implicitly addresses incidents. Even without explicit issues, the continuous cognitive process would still seek actions for further optimization. It could, for example, try to deliver the same services with reduced resources.

Through its reasoning-based core process, the cognitive layer in some examples reaches a high degree of dynamic adaptability to new situations. This is in stark contrast to systems that have been realized through rule-based policies and fixed workflows, where every supported situation needs consideration at design time through suitable branches in the decision tree and diversifying rules. Existing rule-based policies can, however, still be used in the cognitive layer through integration as agents in some examples. This opens an upgrade path from legacy automation, with Al-based models added gradually.

Example use case

To demonstrate how the example cognitive layer works, we have experimented with a use case that optimizes the provisioning decisions for the deployment of a virtual network function (VNF). The source of the intent is an SLA that specifies the service to be delivered along with the KPI targets that have been promised to the customer. Our example use case requires that the VNF be deployed with strict targets on latency and throughput.

We started by developing the required proposer and predictor agents in an offline data science process. We then deployed the service in a test environment and exposed it to a range of usage loads. We also explored deployment options by variating parameters of the underlying TOSCA model. Combined with measured KPIs, these variations create training data sets for learning a model that is capable of connecting deployment options to expected performance. This exercise created a proposal agent that is able to recommend a TOSCA model configuration optimized for the latency and throughput figures required by the SLA.

In our experiment, online fulfillment - from receipt of the SLA to optimized deployment - is fully autonomous and controlled by the reasoning process of the cognitive layer. When the intent derived from the SLA arrives in the knowledge base, the reasoning loop starts processing it. The reasoner finds that the agent learned in the test environment matches the needs and requests a proposal. The agent delivers a TOSCA model fully configured and optimized for the requested KPI target and proposes to deploy accordingly.

The cognitive layer then uses prediction and evaluation agents to assess the proposal. In this case, our prediction agent contains a state-action model - a probabilistic graph based on Markov decision process modeling. The model is continuously learned from observing states and the results of observed actions. It has the ability to estimate the probability of expected result states for proposed actions, enabling informed decisions about actions considering their risk.

In the evaluation step, the autonomous system detects and resolves all remaining conflicts between intents. It also decides on escalations if the risk of the proposed deployment or uncertainty of the models is too high. If escalation is required, the cognitive process requests the support of a human technician, presents the situation including proposals and predictions, and asks for approval. While the cognitive layer can operate fully autonomously, it knows when the human workforce wants to be involved. The exact threshold for escalation is at the discretion of the operator and is determined by behavioral intent. If the system gains human trust as a result of presenting many good actions, the threshold for fully autonomous decisions can be lowered.

When a proposed action is selected after verifying that the impact on all intents is advantageous and risk is reasonably low, it is then handed over for execution to the orchestrator behind the actuation agent. This concludes the first intent-handling loop after the new intent for the new SLA was introduced to the cognitive layer. The gap between the new intent and the current state was particularly wide since the new service was not yet provisioned. This gap narrowed through a provisioning action. Further iterations continuously monitor the service deployment, optimize it when possible or heal when needed. In this way, the continuous cognitive operation process provides fulfillment and assurance of expectations formulated as intents.

Conclusion

Zero-touch autonomy is an ideal beyond reach as long as artificial intelligence (Al) cannot match human capability to reason and decide within complex dependencies and broad domains. However, by using a combination of currently available and well-understood Al techniques within a flexible architecture, it is possible in some examples of this disclosure to reach a high degree of practical autonomous operation.

Our use case example demonstrates how the cognitive layer that we have developed can enable autonomous operation with the use of intents. It is built with an agent architecture that decouples the life cycles of the agents. It is coordinated by knowledge-driven reasoning that composes machine-learned models and legacy implementations. It evaluates action impact on intent fulfillment and makes decisions based on expected gains and risks, while resolving conflicting goals and maximizing utility. It can adapt to new situations through learning. The resulting system can execute many of the cognitive considerations human technicians make when they operate services and networks manually. However, the cognitive layer does this with continuous attention and nearimmediate reaction.

The adaptive reasoning capabilities of the cognitive layer may for example enable effective management of growing network complexity, dramatically reducing the need for humans to manually modify policies or participate in online decision-making. This frees up the human workforce to concentrate on offline tasks such as executing data science processes, optimizing the available models and defining business strategies that are implemented by intent setting.

Figure 28 shows an example of a communication system QQ100 in accordance with some embodiments.

In the example, the communication system QQ100 includes a telecommunication network QQ102 that includes an access network QQ104, such as a radio access network (RAN), and a core network QQ106, which includes one or more core network nodes QQ108. The access network QQ104 includes one or more access network nodes, such as network nodes QQ110a and QQ110b (one or more of which may be generally referred to as network nodes QQ110), or any other similar 3^rd Generation Partnership Project (3GPP) access node or non- 3GPP access point. The network nodes QQ110 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs QQ112a, QQ112b, QQ112c, and QQ112d (one or more of which may be generally referred to as UEs QQ112) to the core network QQ106 over one or more wireless connections.

Example wireless communications over a wireless connection include transmitting and/or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and/or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system QQ100 may include any number of wired or wireless networks, network nodes, UEs, and/or any other components or systems that may facilitate or participate in the communication of data and/or signals whether via wired or wireless connections. The communication system QQ100 may include and/or interface with any type of communication, telecommunication, data, cellular, radio network, and/or other similar type of system.

The UEs QQ112 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and/or operable to communicate wirelessly with the network nodes QQ110 and other communication devices. Similarly, the network nodes QQ110 are arranged, capable, configured, and/or operable to communicate directly or indirectly with the UEs QQ112 and/or with other network nodes or equipment in the telecommunication network QQ102 to enable and/or provide network access, such as wireless network access, and/or to perform other functions, such as administration in the telecommunication network QQ102.

In the depicted example, the core network QQ106 connects the network nodes QQ110 to one or more hosts, such as host QQ116. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network QQ106 includes one more core network nodes (e.g., core network node QQ108) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and/or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node QQ108. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and/or a User Plane Function (UPF).

The host QQ116 may be under the ownership or control of a service provider other than an operator or provider of the access network QQ104 and/or the telecommunication network QQ102, and may be operated by the service provider or on behalf of the service provider. The host QQ116 may host a variety of applications to provide one or more services. Examples of such applications include the provision of live and/or pre-recorded audio/video content, data collection services, for example, retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

As a whole, the communication system QQ100 of Figure 28 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM); Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and/or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and/or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and/or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

In some examples, the telecommunication network QQ102 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network

QQ102 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network QQ102. For example, the telecommunications network QQ102 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and/or Massive Machine Type Communication (mMTC)ZMassive loT services to yet further UEs.

In some examples, the UEs QQ112 are configured to transmit and/or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network QQ104 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network QQ104. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved- UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

In the example illustrated in Figure 28, the hub QQ114 communicates with the access network QQ104 to facilitate indirect communication between one or more UEs (e.g., UE QQ112c and/or QQ112d) and network nodes (e.g., network node QQ110b). In some examples, the hub QQ114 may be a controller, router, a content source and analytics node, or any of the other communication devices described herein regarding UEs. For example, the hub QQ114 may be a broadband router enabling access to the core network QQ106 for the UEs. As another example, the hub QQ114 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes QQ110, or by executable code, script, process, or other instructions in the hub QQ114. As another example, the hub QQ114 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub QQ114 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub QQ114 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub QQ114 then provides to the UE either directly, after performing local processing, and/or after adding additional local content. In still another example, the hub QQ114 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

The hub QQ114 may have a constant/persistent or intermittent connection to the network node QQ110b. The hub QQ114 may also allow for a different communication scheme and/or schedule between the hub QQ114 and UEs (e.g., UE QQ112c and/or QQ112d), and between the hub QQ114 and the core network QQ106. In other examples, the hub QQ114 is connected to the core network QQ106 and/or one or more UEs via a wired connection. Moreover, the hub QQ114 may be configured to connect to an M2M service provider over the access network QQ104 and/or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes QQ110 while still connected via the hub QQ114 via a wired or wireless connection. In some embodiments, the hub QQ114 may be a dedicated hub - that is, a hub whose primary function is to route communications to/from the UEs from/to the network node QQ110b. In other embodiments, the hub QQ114 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node QQ110b, but which is additionally capable of operating as a communication start and/or end point for certain data channels.

Figure 29 shows a UE QQ200 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and/or operable to communicate wirelessly with network nodes and/or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless camera, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop-embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded/integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and/or an enhanced MTC (eMTC) UE.

A UE may support device-to-device (D2D) communication, for example by implementing a 3GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and/or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter). The UE QQ200 includes processing circuitry QQ202 that is operatively coupled via a bus QQ204 to an input/output interface QQ206, a power source QQ208, a memory QQ210, a communication interface QQ212, and/or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in Figure 29. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

The processing circuitry QQ202 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory QQ210. The processing circuitry QQ202 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry QQ202 may include multiple central processing units (CPUs). The processing circuitry QQ202 may be operable to provide, either alone or in conjunction with other UE QQ200 components, such as the memory QQ210, UE QQ200 functionality.

In the example, the input/output interface QQ206 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and/or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE QQ200. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, a sensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

In some embodiments, the power source QQ208 is structured as a battery or battery pack.

Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source QQ208 may further include power circuitry for delivering power from the power source QQ208 itself, and/or an external power source, to the various parts of the UE QQ200 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source QQ208. Power circuitry may perform any formatting, converting, or other modification to the power from the power source QQ208 to make the power suitable for the respective components of the UE QQ200 to which power is supplied.

The memory QQ210 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory QQ210 includes one or more application programs QQ214, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data QQ216. The memory QQ210 may store, for use by the UE QQ200, any of a variety of various operating systems or combinations of operating systems.

The memory QQ210 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and/or ISIM, other memory, or any combination thereof. The UICC may for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory QQ210 may allow the UE QQ200 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory QQ210, which may be or comprise a device-readable storage medium.

The processing circuitry QQ202 may be configured to communicate with an access network or other network using the communication interface QQ212. The communication interface QQ212 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna QQ222. The communication interface QQ212 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter QQ218 and/or a receiver QQ220 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter QQ218 and receiver QQ220 may be coupled to one or more antennas (e.g., antenna QQ222) and may share circuit components, software or firmware, or alternatively be implemented separately.

In some embodiments, communication functions of the communication interface QQ212 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and/or standards, such as IEEE 802.11 , Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol/internet protocol (TCP/IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface QQ212, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting from several sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or controls a robotic arm performing a medical procedure according to the received input. A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are devices which are or which are embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door/window sensor, a flood/moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and/or software in dependence on the intended application of the loT device in addition to other components as described in relation to the UE QQ200 shown in Figure 29.

As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and/or measurements, and transmits the results of such monitoring and/or measurements to another UE and/or a network node. The UE may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-loT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and/or reporting on its operational status or other functions associated with its operation.

In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and/or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

Figure 30 shows a network node QQ300 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and/or operable to communicate directly or indirectly with a UE and/or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)).

Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and/or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

Other examples of network nodes include multiple transmission point (multi-TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell/multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and/or Minimization of Drive Tests (MDTs).

The network node QQ300 includes processing circuitry QQ302, a memory QQ304, a communication interface QQ306, and a power source QQ308, and/or any other component, or any combination thereof. The network node QQ300 may be composed of multiple physically separate components (e.g., a NodeB component and a RNC component, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node QQ300 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node QQ300 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory QQ304 for different RATs) and some components may be reused (e.g., a same antenna QQ310 may be shared by different RATs). The network node QQ300 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node QQ300, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z- wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node QQ300.

The processing circuitry QQ302 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application-specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and/or encoded logic operable to provide, either alone or in conjunction with other network node QQ300 components, such as the memory QQ304, network node QQ300 functionality. For example, the processing circuitry QQ302 may be configured to cause the network node to perform the methods as described with reference to Figure 23.

In some embodiments, the processing circuitry QQ302 includes a system on a chip (SOC). In some embodiments, the processing circuitry QQ302 includes one or more of radio frequency (RF) transceiver circuitry QQ312 and baseband processing circuitry QQ314. In some embodiments, the radio frequency (RF) transceiver circuitry QQ312 and the baseband processing circuitry QQ314 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry QQ312 and baseband processing circuitry QQ314 may be on the same chip or set of chips, boards, or units.

The memory QQ304 may comprise any form of volatile or non-volatile computer-readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)), and/or any other volatile or non-volatile, non-transitory device-readable and/or computer-executable memory devices that store information, data, and/or instructions that may be used by the processing circuitry QQ302. The memory QQ304 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and/or other instructions capable of being executed by the processing circuitry QQ302 and utilized by the network node QQ300. The memory QQ304 may be used to store any calculations made by the processing circuitry QQ302 and/or any data received via the communication interface QQ306. In some embodiments, the processing circuitry QQ302 and memory QQ304 is integrated. The communication interface QQ306 is used in wired or wireless communication of signaling and/or data between a network node, access network, and/or UE. As illustrated, the communication interface QQ306 comprises port(s)/terminal(s) QQ316 to send and receive data, for example to and from a network over a wired connection. The communication interface QQ306 also includes radio front-end circuitry QQ318 that may be coupled to, or in certain embodiments a part of, the antenna QQ310. Radio front-end circuitry QQ318 comprises filters QQ320 and amplifiers QQ322. The radio front-end circuitry QQ318 may be connected to an antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry may be configured to condition signals communicated between antenna QQ310 and processing circuitry QQ302. The radio front-end circuitry QQ318 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry QQ318 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters QQ320 and/or amplifiers QQ322. The radio signal may then be transmitted via the antenna QQ310.

Similarly, when receiving data, the antenna QQ310 may collect radio signals which are then converted into digital data by the radio front-end circuitry QQ318. The digital data may be passed to the processing circuitry QQ302. In other embodiments, the communication interface may comprise different components and/or different combinations of components.

In certain alternative embodiments, the network node QQ300 does not include separate radio front-end circuitry QQ318, instead, the processing circuitry QQ302 includes radio frontend circuitry and is connected to the antenna QQ310. Similarly, in some embodiments, all or some of the RF transceiver circuitry QQ312 is part of the communication interface QQ306. In still other embodiments, the communication interface QQ306 includes one or more ports or terminals QQ316, the radio front-end circuitry QQ318, and the RF transceiver circuitry QQ312, as part of a radio unit (not shown), and the communication interface QQ306 communicates with the baseband processing circuitry QQ314, which is part of a digital unit (not shown).

The antenna QQ310 may include one or more antennas, or antenna arrays, configured to send and/or receive wireless signals. The antenna QQ310 may be coupled to the radio frontend circuitry QQ318 and may be any type of antenna capable of transmitting and receiving data and/or signals wirelessly. In certain embodiments, the antenna QQ310 is separate from the network node QQ300 and connectable to the network node QQ300 through an interface or port.

The antenna QQ310, communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any receiving operations and/or certain obtaining operations described herein as being performed by the network node. Any information, data and/or signals may be received from a UE, another network node and/or any other network equipment. Similarly, the antenna QQ310, the communication interface QQ306, and/or the processing circuitry QQ302 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and/or signals may be transmitted to a UE, another network node and/or any other network equipment.

The power source QQ308 provides power to the various components of network node QQ300 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source QQ308 may further comprise, or be coupled to, power management circuitry to supply the components of the network node QQ300 with power for performing the functionality described herein. For example, the network node QQ300 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source QQ308. As a further example, the power source QQ308 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

Embodiments of the network node QQ300 may include additional components beyond those shown in Figure 30 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and/or any functionality necessary to support the subject matter described herein. For example, the network node QQ300 may include user interface equipment to allow input of information into the network node QQ300 and to allow output of information from the network node QQ300. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node QQ300.

Figure 31 is a block diagram of a host QQ400, which may be an embodiment of the host QQ116 of Figure 28, in accordance with various aspects described herein. As used herein, the host QQ400 may be or comprise various combinations hardware and/or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host QQ400 may provide one or more services to one or more UEs.

The host QQ400 includes processing circuitry QQ402 that is operatively coupled via a bus QQ404 to an input/output interface QQ406, a network interface QQ408, a power source QQ410, and a memory QQ412. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as Figures 29 and 30, such that the descriptions thereof are generally applicable to the corresponding components of host QQ400.

The memory QQ412 may include one or more computer programs including one or more host application programs QQ414 and data QQ416, which may include user data, e.g., data generated by a UE for the host QQ400 or data generated by the host QQ400 for a UE. Embodiments of the host QQ400 may utilize only a subset or all of the components shown. The host application programs QQ414 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (WC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs QQ414 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host QQ400 may select and/or indicate a different host for over-the-top services for a UE. The host application programs QQ414 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

Figure 32 is a block diagram illustrating a virtualization environment QQ500 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may include virtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments QQ500 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. Applications QQ502 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and/or benefits of some of the embodiments disclosed herein.

Hardware QQ504 includes processing circuitry, memory that stores software and/or instructions executable by hardware processing circuitry, and/or other hardware devices as described herein, such as a network interface, input/output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers QQ506 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs QQ508a and QQ508b (one or more of which may be generally referred to as VMs QQ508), and/or perform any of the functions, features and/or benefits described in relation with some embodiments described herein. The virtualization layer QQ506 may present a virtual operating platform that appears like networking hardware to the VMs QQ508.

The VMs QQ508 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer QQ506. Different embodiments of the instance of a virtual appliance QQ502 may be implemented on one or more of VMs QQ508, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

In the context of NFV, a VM QQ508 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs QQ508, and that part of hardware QQ504 that executes that VM, be it hardware dedicated to that VM and/or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs QQ508 on top of the hardware QQ504 and corresponds to the application QQ502.

Hardware QQ504 may be implemented in a standalone network node with generic or specific components. Hardware QQ504 may implement some functions via virtualization. Alternatively, hardware QQ504 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration QQ510, which, among others, oversees lifecycle management of applications QQ502. In some embodiments, hardware QQ504 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system QQ512 which may alternatively be used for communication between hardware nodes and radio units.

Figure 33 shows a communication diagram of a host QQ602 communicating via a network node QQ604 with a UE QQ606 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE QQ112a of Figure 28 and/or UE QQ200 of Figure 29), network node (such as network node QQ110a of Figure 28 and/or network node QQ300 of Figure 30), and host (such as host QQ116 of Figure 28 and/or host QQ400 of Figure 31) discussed in the preceding paragraphs will now be described with reference to Figure 33.

Like host QQ400, embodiments of host QQ602 include hardware, such as a communication interface, processing circuitry, and memory. The host QQ602 also includes software, which is stored in or accessible by the host QQ602 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE QQ606 connecting via an over-the-top (OTT) connection QQ650 extending between the UE QQ606 and host QQ602. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection QQ650.

The network node QQ604 includes hardware enabling it to communicate with the host QQ602 and UE QQ606. The connection QQ660 may be direct or pass through a core network (like core network QQ106 of Figure 28) and/or one or more other intermediate networks, such as one or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

The UE QQ606 includes hardware and software, which is stored in or accessible by UE QQ606 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE QQ606 with the support of the host QQ602. In the host QQ602, an executing host application may communicate with the executing client application via the OTT connection QQ650 terminating at the UE QQ606 and host QQ602. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection QQ650 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection QQ650.

The OTT connection QQ650 may extend via a connection QQ660 between the host QQ602 and the network node QQ604 and via a wireless connection QQ670 between the network node QQ604 and the UE QQ606 to provide the connection between the host QQ602 and the UE QQ606. The connection QQ660 and wireless connection QQ670, over which the OTT connection QQ650 may be provided, have been drawn abstractly to illustrate the communication between the host QQ602 and the UE QQ606 via the network node QQ604, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

As an example of transmitting data via the OTT connection QQ650, in step QQ608, the host QQ602 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE QQ606. In other embodiments, the user data is associated with a UE QQ606 that shares data with the host QQ602 without explicit human interaction. In step QQ610, the host QQ602 initiates a transmission carrying the user data towards the UE QQ606. The host QQ602 may initiate the transmission responsive to a request transmitted by the UE QQ606. The request may be caused by human interaction with the UE QQ606 or by operation of the client application executing on the UE QQ606. The transmission may pass via the network node QQ604, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step QQ612, the network node QQ604 transmits to the UE QQ606 the user data that was carried in the transmission that the host QQ602 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step QQ614, the UE QQ606 receives the user data carried in the transmission, which may be performed by a client application executed on the UE QQ606 associated with the host application executed by the host QQ602.

In some examples, the UE QQ606 executes a client application which provides user data to the host QQ602. The user data may be provided in reaction or response to the data received from the host QQ602. Accordingly, in step QQ616, the UE QQ606 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input/output interface of the UE QQ606. Regardless of the specific manner in which the user data was provided, the UE QQ606 initiates, in step QQ618, transmission of the user data towards the host QQ602 via the network node QQ604. In step QQ620, in accordance with the teachings of the embodiments described throughout this disclosure, the network node QQ604 receives user data from the UE QQ606 and initiates transmission of the received user data towards the host QQ602. In step QQ622, the host QQ602 receives the user data carried in the transmission initiated by the UE QQ606.

In an example scenario, factory status information may be collected and analyzed by the host QQ602. As another example, the host QQ602 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host QQ602 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host QQ602 may store surveillance video uploaded by a UE. As another example, the host QQ602 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host QQ602 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and/or transmitting data.

In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection QQ650 between the host QQ602 and UE QQ606, in response to variations in the measurement results. The measurement procedure and/or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host QQ602 and/or UE QQ606. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection QQ650 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection QQ650 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node QQ604. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host QQ602. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection QQ650 while monitoring propagation times, errors, etc.

Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and/or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and/or performing one or more operations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and/or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and/or by end users and a wireless network generally.

References 1 . Cognitive processes for adaptive intent-based networking,

2. Al-based 5G lifecycle resource management - TM Forum

Claims

1. A method of determining a configuration of a wireless communication network, the method comprising: determining a state of the network; determining a model for predicting a result of applying a configuration to the network; determining one or more objectives for providing one or more services using the network; and determining, based on the state of the network and the model, a configuration for the network that, when applied to the network, satisfies one or more of the objectives.

2. The method of claim 1 , wherein the state of the network comprises one or more of: a current state of the network; a current configuration of the network; a current performance of the network; and/or a forecast of predicted active user, throughput and traffic growth in the network over a predetermined time period.

3. The method of claim 2, wherein the current state of the network includes a cell identifier, ID, of one or more cells in the network and, for each cell, PRB allocation and carrier specifications for the cell.

4. The method of claim 2 or 3, wherein the predicted active user, throughput and traffic growth in the network over the predetermined time period is determined by a forecasting software module.

5. The method of any of claims 1 to 4, wherein the one or more objectives comprise at least one of: one or more intents for providing the one or more services using the network; one or more requirements for the one or more services; and/or one or more constraints for the one or more services.

6. The method of any of claims 1 to 5, wherein determining the configuration for the network comprises: providing the state of the network and the one or more objectives to at least one first software module to generate one or more candidate configurations for the network; and providing the one or more candidate configurations for the network to at least one second software module to predict a result of applying each of the one or more candidate configurations to the network using the model.

7. The method of claim 6, comprising selecting, for the configuration for the network, one of the one or more candidate configurations based on the results of applying each of the one or more candidate configurations to the network.

8. The method of any of claims 1 to 7, wherein the configuration for the network comprises one or more of: a configuration of one or more network slices in the network; a configuration of one or more physical resource blocks (PRBs) in the network; a configuration of one or more antenna sectors in the network; a configuration of one or more PRB partitions in the network; a configuration of intra- and/or inter-band carrier aggregation or load balancing; a configuration of a core network of the network; a configuration of a Radio Access Network (RAN) of the network; a configuration of a backhaul network of the network; and/or a proposal to add one or more cell sites to the network.

9. The method of any of claims 1 to 8, comprising determining whether the configuration for the network impacts one or more previous objectives for providing one or more existing services in the network.

10. The method of claim 9, wherein, if the configuration impacts one or more previous objectives, determining, based on the state of the network and the model, a different configuration for the network that, when applied to the network, satisfies one or more of the objectives.

11 . The method of any of claims 1 to 10, wherein the method is repeated: periodically; upon completion; and/or in response to an event.

12. The method of claim 11 , wherein the event comprises one or more of: a request from a network operator; a request from a customer; a change in one or more objectives for the one or more services; a change in the one or more services; a new service to be provided by the network; and/or a new objective for one or more existing services.

13. A computer program comprising instructions which, when executed on at least one processor, cause the at least one processor to carry out a method according to any of claims 1 to 12.

14. A carrier containing a computer program according to claim 13, wherein the carrier comprises one of an electronic signal, optical signal, radio signal or computer readable storage medium.

15. A computer program product comprising non transitory computer readable media having stored thereon a computer program according to claim 13.

16. Apparatus for determining a configuration of a wireless communication network, the apparatus comprising a processor and a memory, the memory containing instructions executable by the processor such that the apparatus is operable to: determine a state of the network; determine a model for predicting a result of applying a configuration to the network; determine one or more objectives for providing one or more services using the network; and determine, based on the state of the network and the model, a configuration for the network that, when applied to the network, satisfies one or more of the objectives.

17. The apparatus of claim 16, wherein the memory contains instructions executable by the processor such that the apparatus is operable to perform the method of any of claims 2