Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium
<p>Fixed wireless access network topology in the 60 GHz Band. (PoP = point-of-presence, IWF = inter-working function, CPE = customer premises equipment) [<a href="#B3-sensors-23-01056" class="html-bibr">3</a>].</p> "> Figure 2
<p>General tree network architecture with a example of pruning method (<a href="#sec4dot1-sensors-23-01056" class="html-sec">Section 4.1</a>).</p> "> Figure 3
<p>Edge node placement comparison. (<b>a</b>) Leest-Rural. (<b>b</b>) Liege-Urban.</p> "> Figure 4
<p>Use cases simulated areas of interest. (<b>a</b>) Leest—Rural. (<b>b</b>) Liege—Urban. (<b>c</b>) Ghent—Tall Buildings.</p> "> Figure 5
<p>General network planning algorithm.</p> "> Figure 6
<p>Ray tracing algorithm example.</p> "> Figure 7
<p>Use Case 1 solution example. Snapshot of the rural Leest area.</p> "> Figure 8
<p>User coverage and network utilisation in UC 1.</p> "> Figure 9
<p>Distance distribution. (<b>a</b>) Between CPE and ENs. (<b>b</b>) Between ENs.</p> "> Figure 10
<p>Use Case 2 solution example. Snapshot of the urban Liege area.</p> "> Figure 11
<p>User coverage and network utilisation in UC 2.</p> "> Figure 12
<p>Use Case 3 solution example. Snapshot of the urban Ghent area.</p> "> Figure 13
<p>Use case comparison of coverage and network utilisation.</p> ">
Abstract
:1. Introduction
- A hierarchical FWA network planner with ray tracing capabilities to support future bit rate demands of residential users.
- A multi-objective optimisation algorithm that minimises the infrastructure size and power consumption while maintaining the network service quality requirements.
- A study of environmental parameters such as rain and vegetation that affect the propagation channel in the 60 GHz band and its impact on network performance.
- A survey of edge node locations for telecom operators to provide optimal network performance.
2. Fixed Wireless Access
2.1. Network Design
2.2. Planning Tools for FWA Designs
2.2.1. Industrial Focused Tools
2.2.2. Research Focused Tools
2.3. Trials
3. Evaluation Framework
3.1. Network Architecture
- Point of presence (PoP) is the node that connects the wireless backhaul network to the fibre core. We use an PoP location from one of the operator in Belgium. It consists of a tower, 14 m in height, with four antenna boxes covering a 360-degree area.
- Edge nodes (EN) are the ones in charge of serving final users or create a backhaul mesh network with other edge nodes.
- Customer premises equipment (CPE) is the final node in users’ homes
3.2. Node Locations
- Facade only (FA): Places the ENs equipment in the same way as the CPE at 4 m on the wall closest to the street in houses, public buildings, or other constructions.
- Lamp post only (LP): Place the ENs on lamp posts at the height of 4 m. Since detailed information on the lamp post is unavailable, it is assumed that lamp posts are placed at corners or intersections and subsequent ones 50 m along each street until covering the investigated area.
- Facade and the lamp post (FP): also called joint location, it is a mixture of the previous strategies, where nodes could be on walls or lamp posts.
3.3. User Density and Traffic
3.4. Use Cases
3.5. Link Budget and Channel Modelling
4. Network Planning and Optimisation
4.1. Simulation Tool
- STEP 1: In the initialisation, the configuration files are loaded, and the scenarios defined. Those inputs include the following files:
- Shapefile of the environment with the area information, such as buildings, vegetation and streets needed to determine the location of nodes.
- The bit rate information of users, as defined by the requirements of the simulations.
- The technology files describing the information needed to perform the link budget calculation as shown in Section 3.5.
- Other configuration files such as the type of node location and type of environmental restrictions (rain or vegetation).
- STEP 2: The possible EN location generation defines the possible location of the nodes in the environment. It first determines the type of location required on the facades, lamp posts or both. Then, it assigns many possible sites based on a maximum number of ENs and evaluates if those are viable. This evaluation is based on Dijkstra’s shortest path algorithm [78], where all the nodes must have at least one path to the PoP. The connections of each path are calculated based on the path loss between the nodes and minimum supported bit rate (i.e., 2.5 Gbps). Those possible EN locations that do not have a connection to the PoP are removed from the list. This process is called tree pruning and is shown in Figure 2. Then, the CPEs are located on the facades of randomly selected houses, and the bit rate and specific location are assigned to the CPE node.
- STEP 3: Once the location of all the nodes is known, a graph network generation is constructed for both CPEs and ENs nodes. First, a list of possible links from each CPE to all close ENs is created. For each connection, a link budget is calculated based on the requested bit rate and the path loss between nodes. The path loss calculation includes ray tracing and environmental factors such as rain and vegetation. A link is added to a viable link list if it fulfils the network requirements. Similarly, the backhaul network between ENs is created, creating links between ENs and accounting for an aggregated minimum bit rate of 2.5 Gbps for each link.
- STEP 4: The constraints and requirements for the graph network are assigned in this step. First, it identifies the optimisation objectives described in Section 4.3. The rules of each node and link are described in a model that is read by the mixer-integer programming (MIP). In this case, the problem objective is to maximise the number of connected users, while minimising the cost of the network which includes the infrastructure size, the accumulated path loss and the averaged hop count.
- STEP 5: The MIP solver uses the Gurobi Optimiser to solve the multi-optimisation problem based on linear expression to map the objectives of the problem into a possible solution [79]. Once one or several solutions are found, the best one is assigned to the wireless network. In particular, once all the radio resources are allocated, each CPE node is assigned to an EN, and the backhaul network is created, calculating the capacity of each link and aggregating all the traffic up to the PoP node.
- STEP 6: In the last step, the algorithm calculates the network performance metrics, saving them in files for further post-processing. In addition, MATLAB scripts were created to collect and aggregate the results and print them, as shown in the results section.
4.2. Ray Tracing Algorithm
- Ray Launching: Each ray is launched every degrees starting for the azimuth of the transmitter antenna and rotating either clockwise or anti-clockwise depending on the location of the receiver node. In these simulations, the value of is 10 degree.
- Ray tracing: For each ray, two reflections are accounted. Each ray is a construction of consecutive points every meter until the maximum distance is reached, maximum PL is achieved, and the maximum number of rebounds are counted or until a receiver node is reached. For bouncing rays, each reflection is calculated based on the angle of arrival of the ray and the angle of the bouncing wall.
- Ray interception: As each ray is composed of points, each point evaluated is a receiver node within a distance of . If a node is within that radius, a possible link is created for the pair of Tx-RX. Instead, if after 250 points (250 m) an Rx is not intercepted, the next ray is evaluated until the whole antenna aperture is scanned.
4.3. Multi-Objective Methods
4.4. Methodology
4.5. Study Metrics
- Coverage, refers to the percentage of served CPEs compared to all the CPEs in the network.
- Serving Distance, determines the usable distance between node either CPE-EN or between ENs.
- Backhaul Capacity is divided in two parts, the supported capacity, referring to the maximum bit rate the links in the backhaul network could provide, while the served capacity is the actual throughput used in those links.
- Link Count establishes the number of hops needed for a CPE to connect to the PoP.
5. Simulation Results
5.1. UC1: Rural Environment
5.1.1. Rural Coverage
5.1.2. Rural Serving Distance
5.1.3. Rural Backhaul Capacity
5.1.4. Rural Link Count
5.2. UC2: Urban Environment
5.2.1. Urban Coverage
5.2.2. Urban Serving Distance
5.2.3. Urban Backhaul Capacity
5.2.4. Urban Link Count
5.3. UC3: Tall Buildings
5.4. Use Case Network Comparison
5.5. Multi-Ray Bouncing Analysis
6. Conclusions and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
3D | Three-Dimensional |
CPE | Customer Premises Equipment |
CN | Core Network |
EN | Edge Node |
FA | Facade only |
FITU | Fitted International Telecommunication Union |
FL | Facade and the Lamp post |
FR | Frequency Range |
FTTH | Fiber-To-The-Home |
FWA | Fixed Wireless Access |
HR | Heavy Rain |
IRS | Intelligent Reflecting Surface |
IWF | Inter-Working Function |
LP | Lamp post only |
LR | Light Rain |
MAC | Medium Access Control |
MaMIMO | Massive Multiple-Input Multiple-Output |
MC | Monte Carlo |
MILP | Mixed Integer Linear Programming |
MIMO | Multiple-Input Multiple-Output |
MIP | Mixer-Integer Programming |
NIST | National Institute of Standards and Technology |
NR | No Rain |
NV | No Vegetation |
PER | Packet Error Rate |
PL | Path Loss |
PN | Private Network |
PoP | Point-of-Presence |
RAN | Radio Access Networks |
RB | Resource Blocks |
RT | Ray Tracing |
SDN | Software Defined Network |
SDR | Software Defined Radio |
SISO | Single-Input Single-Output |
SNR | Signal-to-Noise Ratio |
SON | Self-Organised Network |
UC | Use Case |
VE | Vegetation |
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Network Planner | Main | Software | Code | Solving | Technologies | 5G | Ref | ||||
---|---|---|---|---|---|---|---|---|---|---|---|
Focus | Type | Language | Method | mmWaves | Cellular | FWA | MaMIMO | RT-GIS | Backhaul | ||
ASSET | Industry | Licensed | - | - | Yes | Yes | Yes | Yes | Yes | Yes | [26] |
Atoll | Industry | Licensed | - | - | Yes | Yes | - | Yes | Yes | Yes | [27] |
Capgemini | Industry | Service Based | - | - | Yes | Yes | - | Yes | - | Yes | [28] |
Cell Designer | Industry | Licensed | - | Stochastic/MC | - | Yes | - | Yes | Yes | Yes | [29] |
Hamina | Industry | Licensed | - | - | - | PN | - | - | - | - | [30] |
Huawei | Industry | Licensed | - | - | Yes | Yes | - | Yes | Yes | Yes | [31] |
iBwave | Industry | Licensed | - | - | Yes | Yes | - | Yes | Yes | - | [32] |
LS Telecom | Industry | Licensed | - | - | Yes | Yes | - | Yes | - | Yes | [33] |
NetSim | Industry | Licensed | C, R | Discrete Event | Yes | Yes | - | Yes | - | Yes | [34] |
Terragraph | Industry | Open Source | Open/R | - | Yes | Yes | Yes | Yes | - | Yes | [1,35] |
5G Air Simulator | Research | Open Source | C++ | Event-driven | - | Yes | - | Yes | - | - | [36] |
5G K-Simsys | Research | Open Source | C++ | Time-driven | Yes | Yes | - | Yes | - | - | [37] |
5G Toolbox | Research | Licensed | MatLAB | - | Yes | Yes | - | Yes | Yes | - | [25,38] |
CGA | Research | Licensed | - | - | Yes | Yes | Yes | - | - | - | [39,40] |
NS-3 (5G-LENA) | Research | Open Source | C++, Phy | Event-driven | Yes | Yes | - | Yes | - | - | [41] |
OMNet++ | Research | Open Source | C++ | Event-driven | - | Yes | - | - | Yes | - | [42,43] |
OpenAirInterface | Research | Open Source | C++ | - | - | Yes | - | Yes | - | - | [44] |
OPNET | Research | Licensed | C++ | Stochastic/MC | Yes | Yes | - | Yes | - | Yes | [45] |
Viena 5G | Research | Open Source | MatLAB | Event-driven | Yes | Yes | - | Yes | - | - | [46,47] |
GRAND Tool | Research | Proprietary | Java | Heuristic/MILP | Yes | Yes | Yes | Yes | Yes | Yes | [6,48,49,50,51,52,53,54] |
Name | City | Environment | Operator Penetration | Size [km] | CPE Total [CPE] | CPE Density [CPE/km] | |
---|---|---|---|---|---|---|---|
UC1 | Last Mile in Flanders | Leest | Rural (150 h/km) | 50% | 1.33 | 100 | 75 |
UC2 | Connected in Wallonia | Liege | Urban (1100 h/km) | 20% | 0.45 | 100 | 220 |
UC3 | Tall Buildings | Ghent | Urban (1100 h/km) | 50% | 1.5 | 12 (96 homes) | 8 (64 homes) |
Parameter | Unit | Value |
---|---|---|
Frequency Band | GHz | 60 (57–71) |
Channel bandwidth | GHz | 2.16 |
TDD duty cycle DL | % | 75 |
TDD duty cycle UL | % | 25 |
Spatial duty cycle | % | 15 |
TX RF Paths | # | 32 |
TX Patches/Path | # | 2 |
TX Patch Gain | dBi | 5 |
TX Subarray Gain | dBi | 2 |
TX Total antenna Gain | dBi | 25.1 |
Total radiated power | dBm | 39 |
TX array feed loss | dB | 2.5 |
Receiver noise figure | dB | 10.2 |
Implementation Margin | dB | 4 |
Azimuth Steering | ° | +−45 |
Elevation Steering | ° | +−30 |
Beam Codebook size | # | 1024 |
MCS | Constellation | Backoff | Datarate | SNR |
---|---|---|---|---|
[dB] | [Mbps] | [dB] | ||
1 | BPSK | 0 | 385 | −1.4 |
2 | BPSK | 0 | 770 | 0.5 |
3 | BPSK | 0 | 960 | 2.0 |
4 | BPSK | 0 | 1150 | 3.5 |
5 | BPSK | 0 | 1250 | 4.2 |
6 | BPSK | 0 | 1350 | 5.5 |
7 | QPSK | 2 | 1540 | 3.5 |
8 | QPSK | 2 | 1930 | 4.8 |
9 | QPSK | 2 | 2310 | 6.5 |
10 | QPSK | 2 | 2500 | 7.5 |
11 | QPSK | 2 | 2700 | 8.5 |
12 | 16QAM | 4 | 3080 | 9.5 |
13 | 16QAM | 4 | 3850 | 11.5 |
14 | 16QAM | 4 | 4620 | 12.8 |
15 | 16QAM | 4 | 5000 | 13.8 |
16 | 16QAM | 4 | 5390 | 15.5 |
17 | 64QAM | 6 | 4620 | 14.5 |
18 | 64QAM | 6 | 5780 | 16.5 |
19 | 64QAM | 6 | 6390 | 18.5 |
20 | 64QAM | 6 | 7500 | 19.5 |
21 | 64QAM | 6 | 8080 | 21.0 |
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Castellanos, G.; De Beelde, B.; Plets, D.; Martens, L.; Joseph, W.; Deruyck, M. Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium. Sensors 2023, 23, 1056. https://doi.org/10.3390/s23031056
Castellanos G, De Beelde B, Plets D, Martens L, Joseph W, Deruyck M. Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium. Sensors. 2023; 23(3):1056. https://doi.org/10.3390/s23031056
Chicago/Turabian StyleCastellanos, German, Brecht De Beelde, David Plets, Luc Martens, Wout Joseph, and Margot Deruyck. 2023. "Evaluating 60 GHz FWA Deployments for Urban and Rural Environments in Belgium" Sensors 23, no. 3: 1056. https://doi.org/10.3390/s23031056