CN115175199A - Industrial Internet of things service quality guarantee and frequency spectrum sharing method based on communication integration - Google Patents

Abstract

The invention discloses a synaesthesia-integrated service quality assurance and spectrum sharing method for the Industrial Internet of Things, comprising: in the underlying cognitive radio network, when a primary user and a secondary user transmit at the same time, the secondary user adopts a strategy of constant transmit power ; In the bottom cognitive radio network, when the primary user and the secondary user transmit at the same time, the secondary user adopts the strategy of adaptive transmit power; The strategy of slot allocation; in the top-level cognitive radio network, the strategy of adaptive time slot allocation is adopted when the primary user and the secondary user are transmitted separately in the time domain. The method of the present invention can maximize the throughput of the secondary users in the bottom layer cognitive radio network and the top layer cognitive radio network, and satisfy the service quality requirements of the primary users at the same time.

Description

Synaesthesia-based Service Quality Assurance and Spectrum Sharing Method for Industrial Internet of Things

technical field

The invention belongs to the research field of the integration of communication and perception of the industrial Internet of things, and relates to a service quality assurance and spectrum sharing method of the industrial Internet of things based on synaesthesia integration.

Background technique

Over the past decade, cognitive radio has proven to be an efficient and promising technology to solve the problems of spectrum shortage and underutilization, which can effectively fill the spectrum holes caused by static spectrum allocation. In traditional cognitive radio networks, secondary users are allowed to use the authorized spectrum of the primary user, but in order to protect the privileges of the primary user, the primary user is required not to perceive the existence of the secondary user, which restricts the secondary user and results in the inability to effectively utilize the spectrum. Spectrum sensing is performed by secondary users to find spectrum holes, but spectrum sensing is not accurate enough to avoid secondary users' interference to primary users, which reduces the channel quality of primary users.

To further improve the effectiveness of spectrum utilization, one class of studies relaxes restrictions on secondary users and engages primary users in sensing transmissions in wireless networks. On the one hand, the primary user and the secondary user share the channel information, so that the secondary user can use the corresponding spectrum according to the information; on the other hand, the primary user and the secondary user jointly determine the cooperative transmission scheme, optimize the performance of the secondary user, and ensure the User benefit. While these research efforts provide new ways of using spectrum, several key issues regarding the cooperation of primary and secondary users have not been thoroughly investigated. First, the influence of secondary users on the primary user is usually described by interference power, which cannot directly and accurately reflect the service quality of the primary user; second, the service quality requirements of the primary user may vary significantly due to different service types. Therefore, a unified framework should be built to formulate policies for secondary users based on the quality of service requirements of the primary user; finally, under the constraints of the primary user's service quality, how much resources are shared between the primary user and the secondary user, and how the secondary user helps the primary user relay Transfer more resources.

In order to solve the above problems, it is necessary to propose a secondary user spectrum access method that can protect the quality of service of the primary user in the cooperative cognitive radio network, which is a very feasible direction.

SUMMARY OF THE INVENTION

The purpose of the present invention is to overcome the above-mentioned shortcomings of the prior art, and to provide a method for quality of service assurance and spectrum sharing based on the integration of synaesthesia in the Industrial Internet of Things, which can achieve maximum performance in the bottom-layer cognitive radio network and the top-layer cognitive radio network. The throughput of secondary users can be optimized, and the service quality requirements of primary users can be met at the same time.

In order to achieve the above purpose, the method for quality of service assurance and spectrum sharing based on the integration of synaesthesia for the Industrial Internet of Things according to the present invention includes:

In the underlying cognitive radio network, when the primary user and the secondary user transmit at the same time, the secondary user adopts the strategy of constant transmit power;

In the underlying cognitive radio network, when the primary user and the secondary user transmit at the same time, the secondary user adopts the strategy of adaptive transmit power;

In the top-level cognitive radio network, the fixed time slot allocation strategy is adopted when the primary user and the secondary user are transmitted separately in the time domain;

In the top-level cognitive radio network, the strategy of adaptive time slot allocation is adopted when the primary user and the secondary user are transmitted separately in the time domain.

Also includes:

The service quality requirement of the primary user is described by the effective capacity index C(θ), where θ represents the service quality index, where the larger θ is, the stricter the delayed service quality requirement is.

During simultaneous transmission of primary and secondary users in the underlying cognitive radio network:

The primary user link and the secondary user link work simultaneously;

The transmission power of the primary user remains unchanged, and the transmission power of the secondary user is constant or dynamically adjusted;

The primary user and the secondary user adjust the transmission rate in each time frame according to the instantaneous interference between each other;

Primary user and secondary user receivers decode independently.

When the primary user and the secondary user transmit at the same time in the underlying cognitive radio network, the specific process of the secondary user adopting the strategy of constant transmit power is as follows:

With the goal of maximizing the average throughput of the secondary users, meeting the effective capacity requirements of the primary users, and limiting the average transmit power of the secondary users, an optimization problem is constructed, and then the optimization problem is solved to obtain the optimal transmit power of the secondary users.

When the primary user and the secondary user transmit at the same time in the underlying cognitive radio network, the specific process of the secondary user adopting the strategy of adaptive transmit power is as follows:

With the goal of maximizing the average throughput of the secondary users, meeting the effective capacity requirements of the primary user, limiting the average transmit power of the secondary users, and limiting the instantaneous upper bound of the transmit power of the secondary users, an optimization problem is constructed, and then the optimization problem is solved to obtain The optimal transmit power scheme for secondary users, wherein the optimization problem is converted into a convex optimization problem based on optimization theory and probabilistic transmission strategy, and then the Lagrangian equation of the optimization problem is solved based on KKT conditions, and numerical search The method obtains Lagrangian multipliers to obtain the optimal transmit power scheme of secondary users.

During the time domain separation transmission process of primary user and secondary user in the top-level cognitive radio network:

Both the primary user transmitter and the secondary user transmitter use constant power to transmit data;

The primary user transmitter and the secondary user transmitter transmit data in a time-domain separated manner;

The entire time slot is equally divided into two stages, and each stage is divided into two time slots, marked as time slot 1, time slot 2, time slot 3 and time slot 4, where the time of time slot 1 and time slot 3 The lengths are equal, and the time lengths of time slot 2 and time slot 4 are equal;

The time scale of slot 1 is fixed or dynamically adjusted.

In the top-level cognitive radio network, the specific process of adopting a fixed time slot allocation strategy when the primary user and the secondary user are separated in the time domain is as follows: to maximize the average throughput of the secondary users, meet the effective capacity requirements of the primary user, and make the primary user The average transmit power of users and secondary users is constant, and the time ratio of time slot 1 under any channel state information is unchanged as the goal, and an optimization problem is constructed, and then the optimization problem is solved to obtain the optimal time ratio of time slot 1.

The specific process of adopting the strategy of adaptive time slot allocation when the primary user and the secondary user in the top-level cognitive radio network separate transmission in the time domain are as follows:

With the goal of maximizing the average throughput of the secondary users, meeting the effective capacity requirements of the primary user, and making the average transmit power of the primary user and the secondary user constant, an optimization problem is constructed, and then the optimization problem is solved to obtain the optimal adaptive time slot distribution plan;

Wherein, the Lagrangian equation of the optimization problem is solved based on the KKT condition, and the Lagrangian multiplier is obtained by a numerical search method, so as to obtain the optimal adaptive time slot allocation scheme.

The present invention has the following beneficial effects:

In the specific operation of the synaesthesia-based service quality assurance and spectrum sharing method for the Industrial Internet of Things of the present invention, the primary user and the secondary user share the channel and service quality information, adjust the transmission rate, and adjust the resource allocation strategy to cooperate with each other. In this way, the transmission strategy is jointly designed to maximize the throughput of the secondary user while meeting the service quality requirements of the primary user. Compared with the traditional cognitive radio network, the present invention has more sufficient spectrum utilization, can directly reflect the service quality status of the primary user, can obtain how the secondary user controls its transmission power, and can obtain how much resources are shared by the primary user and the secondary user.

Description of drawings

Fig. 1 is a system model diagram of a cooperative cognitive radio network;

2 is a schematic diagram of a cooperation protocol in a top-level cognitive radio network in the present invention;

3 is a graph showing the relationship between the secondary user average throughput and the primary user service quality index θ in the present invention;

4 is a graph showing the relationship between the average power consumption of secondary users and the service quality index θ of primary users in the present invention;

5 is a schematic diagram of how many timeslot resources will be used when secondary users cooperate in the present invention;

6 is a graph showing the relationship between the secondary user average throughput and the primary user transmit power budget in the present invention;

FIG. 7 is a graph showing the relationship between the average throughput of the secondary user and the transmit power budget of the secondary user in the present invention.

Detailed ways

In the synaesthesia-based service quality assurance and spectrum sharing method for the Industrial Internet of Things of the present invention, the primary user and the secondary user share the channel and service quality information, adjust the transmission rate, and adjust the resource allocation strategy in a collaborative manner. Design a transmission strategy. The present invention includes two cognitive radio networks: in the underlying cognitive radio network, there is cross-interference between the transceiver transmissions of the primary user and the secondary user, and the secondary user needs to control its transmit power without violating the primary user's Quality of service constraints; in the top-level cognitive radio network, the primary user and the secondary user transmit through time domain separation to avoid cross-interference. User passes data.

Referring to FIG. 1 and FIG. 2 , the method for quality of service assurance and spectrum sharing based on synaesthesia integration of the Industrial Internet of Things according to the present invention includes the following contents:

1) Build a model for simultaneous transmission of primary and secondary users in the underlying cognitive radio network:

The specific process of step 1) is:

1a) The primary user link and the secondary user link work simultaneously;

1b) The transmit power of the primary user transmitter remains unchanged, and the secondary user transmitter dynamically adjusts the transmit power;

1c) The physical signal transmission model is:

1d) The primary user and the secondary user adjust the transmission rate in each time frame according to the instantaneous interference between each other. The transmission rate (nats/frame) of the primary user link and the secondary user link is:

P₂随信道状态信息h变化；x_p及x_s分别为主用户链路和次级用户链路传输的复杂信号；y_pr及y_sr分别为主用户接收机和次级用户接收机的接收信号；n_pr及n_sr为独立循环对称复高斯噪声，方差为σ²；h₁₁、h₁₂、h₂₂、h₂₁及h_ps分别为主用户发射机—主用户接收机、主用户发射机—次级用户接收机、次级用户发射机—次级用户接收机、次级用户发射机—主用户接收机、主用户发射机—次级用户发射机之间信道的功率增益；Among them, P ₁ and P ₂ are the transmit powers of the primary user transmitter and the secondary user transmitter respectively;

P ₂ varies with the channel state information h; x _p and x _s are the complex signals transmitted by the primary user link and the secondary user link, respectively; y _pr and y _sr are received by the primary user receiver and the secondary user receiver, respectively Signal; n _pr and n _sr are independent cyclic symmetric complex Gaussian noise with variance σ ² ; h ₁₁ , h ₁₂ , h ₂₂ , h ₂₁ and h _ps are respectively the main user transmitter—the main user receiver and the main user transmitter - the power gain of the channel between secondary user receiver, secondary user transmitter - secondary user receiver, secondary user transmitter - primary user receiver, primary user transmitter - secondary user transmitter;

1e) There is no complex scheduling and coordination process between the primary user and secondary user transmitters, so the primary user and secondary user receivers are decoded independently, with a simple decoder and protocol structure;

2) When the primary user and the secondary user transmit at the same time in the underlying cognitive radio network, the secondary user adopts the strategy of constant transmit power;

The specific process of step 2) is:

2a) Obtain the optimal transmit power of the secondary user by solving the optimization problem A1

为次级用户平均发射功率预算；

为主用户有效容量。in,

is the average transmit power budget for secondary users;

Effective capacity for the primary user.

为以下方程的解：2b) According to the above constraints, let

is the solution of the following equation:

为：2c) The optimal solution of A1

for:

3) When the primary user and the secondary user transmit at the same time in the underlying cognitive radio network, the secondary user adopts the strategy of adaptive transmit power;

The specific process of step 3) is:

3a) Construct the optimization problem A2 to obtain the optimal transmit power scheme of the secondary user;

Among them, P _upper is the upper bound of instantaneous power;

3b) A2 is not a convex optimization problem, correct it to obtain a convex optimization problem A2_a.

Constraint 1) in A2 can be written as:

g(P₂)单调递减，且有一个拐点(P,g(P))。in,

g(P ₂ ) decreases monotonically and has an inflection point (P,g(P)).

为上边界函数，是凹函数。基于优化理论和概率传输策略，用直线段代替g(P₂)的非凹区域，将非凹函数g(P₂)转为凹函数

使优化问题易于处理。U is the convex hull formed by (P ₂ ,g(P ₂ )),

is the upper boundary function, which is a concave function. Based on optimization theory and probability transfer strategy, the non-concave region of g(P ₂ ) is replaced by a straight line segment, and the non-concave function g(P ₂ ) is converted into a concave function

Make optimization problems tractable.

According to the function g(P ₂ ), P ₂ ∈[0,P _up ], it can be obtained in three cases

①Case 1: g(P ₂ ) is under the straight line segment connected by (0,g(0)) and (P _up ,g(P _up )), the straight line segment constitutes the upper boundary as follows:

The maximum achievable transfer rates for secondary users are:

使由

和(P_up,g(P_up))连接的直线为g(P₂)在

的切线，直线段和

内的g(P₂)组成上边界：②Case 2: (P,g(P)) is the only inflection point within [0,P _up ], which can be uniquely found

make by

The straight line connecting with (P _up ,g(P _up )) is g(P ₂ ) in

tangents, straight line segments and

g(P ₂ ) inside constitutes the upper bound:

The maximum achievable transfer rates for secondary users are:

③Case 3: (P,g(P)) is the only inflection point in [0, _Pup ], if _Pup <P, then _g (P2) is a concave function in [0, _Pup ], g(Pup ₂ ) is to form the upper boundary:

The maximum achievable transfer rates for secondary users are:

构建修正的凸优化问题A2_a：3c) Replace g(P ₂ ) with

Construct the revised convex optimization problem A2_a:

The Lagrange equation of A2_a is:

L ₁ =E{J ₁ }

λ和η为约束1)和2)的拉格朗日乘子。in,

λ and η are Lagrange multipliers for constraints 1) and 2).

A2_a is a convex optimization function, and the optimal solution satisfies:

和

恒定，分别用

和

表示，则有：For case one,

and

constant, respectively

and

means, there are:

的闭式表达式不存在，可以通过数值搜索的方法获得。For cases two and three,

The closed-form expression does not exist and can be obtained by numerical search.

后，基于KKT条件，选择拉格朗日乘子

和

以满足：3d) get

Then, based on the KKT condition, choose the Lagrange multiplier

and

I'm satisfied:

和

由数值搜索获得，由梯度下降优化拉格朗日对偶问题，以获得

和

and

Obtained by numerical search, the Lagrangian dual problem is optimized by gradient descent to obtain

and

4) The model of time-domain separation of primary user and secondary user in the top-level cognitive radio network:

The specific process of step 4) is:

4a) both the primary user transmitter and the secondary user transmitter transmit data with constant power;

4b) The entire time slot is equally divided into two stages, and each stage is further divided into two time slots, which are marked as time slots 1 to 4, wherein the lengths of time slots 1 and 3 are equal, and the lengths of time slots 2 and 4 are equal;

4c) Data transmission method at the top level: the primary user transmitter transmits its own data in the first stage, with a higher rate in time slot 1 and a lower rate in time slot 2; in time slot 1, the secondary user The transmitter can receive and store the information transmitted by the primary user's transmitter; in time slot 3, the secondary user's transmitter power amplifies and relays the signal to the primary user's receiver. Therefore, the primary user receiver receives two copies of the signal, each experiencing different channel fading. Accordingly, the primary user receiver processes the two copies of the signal by maximal ratio combining. Finally in time slot 4, the secondary user transmitter transmits its own data.

4d) The physical layer transmission signal is:

Among them, y _pr,i , y _ss,i and y _sr,i are the received signals of the primary user receiver, the secondary user transmitter and the secondary user receiver in the i-th time slot, respectively; x _p,i is The transmission signal of the primary user in the ith time slot; n _pr,i , n _ss,i and n _sr,i are all independent cyclic symmetric complex Gaussian noises.

5) When the primary user and the secondary user in the top-level cognitive radio network transmit separately in the time domain, a fixed time slot allocation strategy is adopted;

The specific process of step 5) is:

5a) Construct the optimization problem B1 to obtain the optimal time ratio of time slot 1

固定

fixed

where R ₁ (α) and R ₂ (α) are the achievable rates of the primary user and the secondary user in one frame, respectively.

5b) If

最大化次级用户的信号传输时隙长度。It means that even without cooperation, the service quality requirements of the primary user can be satisfied, and the optimal solution

Maximize the signal transmission slot length for secondary users.

like

说明即使第二阶段均协作传输，主用户服务质量需求仍无法满足。the optimal solution

It shows that even if the second stage is all cooperative transmission, the service quality requirement of the primary user cannot be satisfied.

In addition to the above two cases, the unique optimal solution is obtained by the following equation

6) In the top-level cognitive radio network, when the primary user and the secondary user are transmitted separately in the time domain, the strategy of adaptive time slot allocation is adopted;

The specific process of step 6) is:

6a) Formulate the optimization problem B2 to obtain the optimal time slot allocation scheme:

where α(h) is the time ratio of slot 1.

6b) The Lagrangian equation of B2 is:

L ₂ =E{J ₂ }

μ为与约束条件相关的拉格朗日乘子。in,

μ is the Lagrange multiplier associated with the constraints.

Under boundary conditions α∈[0,1], solve the following equations

The optimal α ^* is obtained as:

where [·] ⁺ =max{0,·}.

6c) Choose a μ to make the constraint 1) in B2 equal. By optimizing the Lagrangian dual problem of B2, the Lagrangian multiplier can be obtained by using the gradient descent method.

Simulation test

It can be seen from Figure 3 that in the underlying cognitive radio network, the adaptive power transmission strategy is better than the constant power transmission strategy; the stricter the QoS requirement of the primary user, the smaller the attainable throughput of the secondary user when the constant power transmission strategy is used; The adaptive power allocation scheme is less sensitive to changes in the quality of service of the primary user; when the quality of service of the primary user is very strict, both constant power transmission and adaptive power transmission have zero throughput; when the quality of service is very high or very low, the two The differences of the time slot allocation schemes disappear; the time slot allocation algorithm of the top model is better than the power allocation strategy of the bottom model.

It can be seen from Figure 4 that the stricter the primary user's quality of service constraint is, the less the secondary user's transmitter power is.

It can be seen from Figure 5 that when the service quality requirements are relatively loose, the primary user's service quality requirements can be met without cooperation; when the service quality requirements are very strict, the secondary users need to use all resources to relay and transmit the primary user resources, so that the average α is equal to 1. .

It can be seen from FIG. 6 that the increase of the transmit power of the primary user increases the interference to the secondary user, and at the same time, the attainable throughput of the secondary user increases to achieve better performance.

It can be seen from Fig. 7 that the increase of the transmit power of the secondary user will increase the achievable throughput of the secondary user and achieve better performance.

Claims (8)

1. A kind of service quality assurance and spectrum sharing method based on synaesthesia integration of industrial Internet of things, it is characterized in that, comprising: In the underlying cognitive radio network, when the primary user and the secondary user transmit at the same time, the secondary user adopts the strategy of constant transmit power; In the underlying cognitive radio network, when the primary user and the secondary user transmit at the same time, the secondary user adopts the strategy of adaptive transmit power; In the top-level cognitive radio network, the fixed time slot allocation strategy is adopted when the primary user and the secondary user are transmitted separately in the time domain; In the top-level cognitive radio network, the strategy of adaptive time slot allocation is adopted when the primary user and the secondary user are transmitted separately in the time domain. 2. The method for quality of service assurance and spectrum sharing based on synaesthesia integration of the Industrial Internet of Things according to claim 1, characterized in that, further comprising: The service quality requirement of the primary user is described by the effective capacity index C(θ), where θ represents the service quality index, where the larger θ is, the stricter the delayed service quality requirement is. 3. The method for quality of service assurance and spectrum sharing based on synaesthesia integration of the Industrial Internet of Things according to claim 1, is characterized in that, in the simultaneous transmission process of the primary user and the secondary user in the underlying cognitive radio network: The primary user link and the secondary user link work simultaneously; The transmission power of the primary user remains unchanged, and the transmission power of the secondary user is constant or dynamically adjusted; The primary user and the secondary user adjust the transmission rate in each time frame according to the instantaneous interference between each other; Primary user and secondary user receivers decode independently. 4. The method for quality of service assurance and spectrum sharing based on synesthesia integration of the Industrial Internet of Things according to claim 1, characterized in that, when the primary user and the secondary user transmit at the same time in the underlying cognitive radio network, the secondary user adopts a constant The specific process of the transmit power strategy is as follows: With the goal of maximizing the average throughput of the secondary users, meeting the effective capacity requirements of the primary users, and limiting the average transmit power of the secondary users, an optimization problem is constructed, and then the optimization problem is solved to obtain the optimal transmit power of the secondary users. 5. The method for quality of service assurance and spectrum sharing based on synaesthesia integration for the Industrial Internet of Things according to claim 1, wherein when the primary user and the secondary user in the underlying cognitive radio network transmit at the same time, the secondary user adopts the automatic transmission method. The specific process of adapting the transmit power strategy is as follows: With the goal of maximizing the average throughput of the secondary users, meeting the effective capacity requirements of the primary user, limiting the average transmit power of the secondary users, and limiting the instantaneous upper bound of the transmit power of the secondary users, an optimization problem is constructed, and then the optimization problem is solved to obtain The optimal transmit power scheme for secondary users, wherein the optimization problem is converted into a convex optimization problem based on optimization theory and probabilistic transmission strategy, and then the Lagrangian equation of the optimization problem is solved based on KKT conditions, and numerical search The method obtains Lagrangian multipliers to obtain the optimal transmit power scheme of secondary users. 6. The method for quality of service assurance and spectrum sharing based on synaesthesia integration of the Industrial Internet of Things according to claim 1, characterized in that, in the top-level cognitive radio network, in the time domain separation transmission process of the primary user and the secondary user: Both the primary user transmitter and the secondary user transmitter use constant power to transmit data; The primary user transmitter and the secondary user transmitter transmit data in a time-domain separated manner; The entire time slot is equally divided into two stages, and each stage is divided into two time slots, marked as time slot 1, time slot 2, time slot 3 and time slot 4, where the time of time slot 1 and time slot 3 The lengths are equal, and the time lengths of time slot 2 and time slot 4 are equal; The time scale of slot 1 is fixed or dynamically adjusted. 7. The method for quality of service assurance and spectrum sharing based on synaesthesia integration in the Industrial Internet of Things according to claim 1, characterized in that, in the top-level cognitive radio network, the primary user and the secondary user adopt a fixed time-domain separation transmission method. The specific process of the time slot allocation strategy is as follows: The optimization problem is constructed with the goal of maximizing the average throughput of secondary users, meeting the effective capacity requirements of primary users, making the average transmit power of primary and secondary users constant, and the time ratio of time slot 1 unchanged under any channel state information. The optimization problem is then solved to obtain the optimal time scale for slot 1. 8. The method for quality of service assurance and spectrum sharing based on synaesthesia integration for the Industrial Internet of Things according to claim 1, wherein the primary user and the secondary user in the top-level cognitive radio network adopt self-adaptation when transmitting separately in the time domain. The specific process of the time slot allocation strategy is as follows: With the goal of maximizing the average throughput of the secondary users, meeting the effective capacity requirements of the primary user, and making the average transmit power of the primary user and the secondary user constant, an optimization problem is constructed, and then the optimization problem is solved to obtain the optimal adaptive time slot distribution plan; Wherein, the Lagrangian equation of the optimization problem is solved based on the KKT condition, and the Lagrangian multiplier is obtained by a numerical search method, so as to obtain the optimal adaptive time slot allocation scheme.

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