A Wireless Network Coverage Analysis Method Considering the Obstruction Effect

Abstract

This invention discloses a wireless network coverage analysis method considering occlusion effects, comprising: constructing a communication network system model for an industrial IoT scenario; calculating the network coverage probability of the communication network system model; and analyzing the variation trend of network coverage probability with access point density under different occlusion losses, obstacle density, obstacle height, and access point height. This invention calculates the coverage probability of an industrial IoT scenario considering occlusion effects and comprehensively considers the impact of occlusion losses, obstacle density, obstacle height, and access point height on the coverage probability, providing optimal access point deployment density under different conditions, which has guiding significance for network planning research.

Description

Technical Field

[0001] This invention relates to the field of wireless communication technology, and in particular to a method for wireless network coverage analysis that takes into account occlusion effects. Background Technology

[0002] The development of fifth-generation and sixth-generation communication technologies has revolutionized industry, enabling network connectivity in industrial settings and thereby improving productivity and economic efficiency. To enhance the quality of wireless communication systems in Industrial Internet of Things (IIoT) scenarios and meet the demands of industrial production, sound network planning is essential. Modern industry's high requirements for coverage, capacity, and reliability are crucial indicators for designing stable and efficient communication networks.

[0003] Based on the requirements of the communication network, a corresponding network planning problem can be constructed and solved to obtain a suitable network deployment scheme. Existing network planning research often uses heuristic algorithms to solve network planning problems, such as genetic algorithms and particle swarm optimization. The advantage of this type of algorithm is its simplicity and ease of implementation, applicable to various optimization problems, but it is prone to getting trapped in local optima and is difficult to provide a globally optimal solution. Many studies have also focused on transforming optimization problems into convex optimization problems and then using convex optimization solvers to solve them. Common methods include decomposing the original problem into multiple convex subproblems and using the Lagrange method to process the original problem. By transforming non-convex optimization problems into convex optimization problems, the difficulty of solving them can be significantly reduced. However, the transformation process often involves relaxing the optimization conditions, which leads to a certain deviation between the obtained solution and the expected optimal solution. The above methods only provide feasible algorithmic solutions and lack theoretical analysis. By deriving the expression of the performance index of the communication network, such as coverage probability, the impact of different parameter changes on it can be analyzed, thereby providing guidance for network planning. Some scholars have derived the coverage probability of communication networks considering the occlusion effect of obstacles. However, since the modeling of obstacles does not conform to the actual situation of IIoT scenarios, these derivations cannot be applied to network planning research in IIoT scenarios.

[0004] Industrial scenarios typically have abundant scatterers that can obstruct transmission paths, necessitating modeling of their effects. However, the obstruction effects of obstacles in IIoT scenarios are currently understudied, and theoretical analyses of IIoT network deployments are quite limited. Summary of the Invention

[0005] Purpose of the invention: To address the above problems, the purpose of this invention is to provide a wireless network coverage analysis method that takes into account the occlusion effect.

[0006] Technical solution: The present invention provides a wireless network coverage analysis method considering occlusion effects, comprising:

[0007] Construct a communication network system model for an industrial Internet of Things (IoT) scenario. This communication network system model includes obstacles, access points, and network communication devices.

[0008] Calculate the network coverage probability of a communication network system model;

[0009] The variation trend of network coverage probability with access point density under different occlusion losses, obstacle density, obstacle height, and access point height is analyzed.

[0010] Furthermore, the communication network system model for industrial IoT scenarios includes:

[0011] A circular region with radius R, centered on a typical device, is constructed as an industrial IoT scenario. Within this scenario, the horizontal positions of access points and obstacles both follow a two-dimensional Poisson process, with densities denoted as λ. AP and λ B The horizontal positions of other network devices follow a Poisson point distribution; the height of a typical device is denoted as h0, and the height difference between a typical device and the access point is denoted as a constant Δh; each obstacle is modeled as a cube, and the average length and average width of all cubes are denoted as h0. and Highly obedient to the mean The exponential distribution;

[0012] The path gain from the i-th access point to a typical device is calculated using the following formula:

[0013]

[0014] In the formula, r represents the horizontal distance between the access point and the typical device. The path gain constant is given at a reference distance d0, where f is the frequency, c is the speed of light, α is the path loss exponent, and G is the path gain constant. i The channel gain is caused by small-scale effects and follows a gamma distribution; S i This is the power loss factor caused by the occlusion effect. When there is a line-of-sight path between the i-th access point and the typical device, S i =1 indicates no power loss; otherwise, S i =δ, 0≤δ<1;

[0015] Associate a typical device with the access point closest to it in line of sight.

[0016] Furthermore, calculating the network coverage probability of the communication network system model includes:

[0017] Calculate the power P received by a typical device from an associated access point. r The calculation formula is:

[0018]

[0019] In the formula, P t Here, x is the transmit power, x is the horizontal distance from the typical device to the associated access point, G0 is the corresponding small-scale fading, and S0 is the corresponding blocking loss. Since the typical device is associated with the access point at the nearest line of sight, S0 = 1.

[0020] The signal-to-interference-plus-noise ratio (SIR / NNR) is calculated using the following formula:

[0021]

[0022] In the formula, It is noise power, I L It's interference from the line-of-sight access point, I N The interference is from non-line-of-sight access points;

[0023] Among them, I L and I N The calculation formulas are as follows:

[0024]

[0025] In the formula, and Let S represent the sets of line-of-sight access points and non-line-of-sight access points, respectively; for all line-of-sight access points, S i =1, for all non-line-of-sight access points, S i =δ;

[0026] The formula for calculating the network coverage probability is as follows:

[0027]

[0028] In the formula, T is the signal-to-interference-plus-noise ratio threshold, and P... L P(γ>T) is the probability that there is at least one line-of-sight associated point, and P(γ>T) is the probability that the signal-to-interference-plus-noise ratio is greater than the threshold T given that there is at least one line-of-sight associated point. It represents the probability that an obstacle on a horizontal link will obstruct the communication link; parameter parameter ω is the mean channel gain caused by small-scale effects.

[0029] Furthermore, the analysis of the variation trend of network coverage probability with access point density under different occlusion losses, obstacle density, obstacle height, and access point height includes:

[0030] Step 301: Substitute the system parameter values ​​into the formula for calculating the coverage probability, including the obstacle density λ. B The height difference Δh between the device and the access point, and the average length of the obstacle. Average width average height Path loss exponent α, power loss δ at non-line-of-sight access points, and noise power The signal-to-interference-plus-noise ratio threshold T;

[0031] Step 302: Change the value of a certain parameter within a certain range. The parameters to be changed include the power loss δ of the non-line-of-sight access point and the obstacle density λ. B Average height of obstacles In addition to the height difference Δh between the device and the access point, keeping other parameters constant, calculate the access point density λ for different values ​​of these parameters. AP The coverage probability below;

[0032] Step 303: Analyze the coverage probability as a function of access point density λ under different parameters. AP The changing trend of parameters was analyzed to assess their impact on the coverage probability.

[0033] Beneficial effects: Compared with the prior art, the significant advantages of this invention are:

[0034] This invention proposes a model suitable for IIoT networks that considers the occlusion effect of obstacles. By calculating the coverage probability under different occlusion losses, obstacle densities, average obstacle heights, and height differences between devices and access points (APs), the impact of these parameters on the coverage probability can be clearly analyzed, providing guidance for network planning in IIoT scenarios. This is significant for determining the number of APs and adjusting AP heights during network planning, thereby avoiding the enormous workload caused by exhaustive search. Attached Figure Description

[0035] Figure 1 A flowchart of a wireless network coverage analysis method that takes into account occlusion effects;

[0036] Figure 2 A comparison of coverage probability versus AP density curves under different shading losses;

[0037] Figure 3 A comparison of coverage probability versus AP density curves under different obstacle densities;

[0038] Figure 4 A comparison of coverage probability versus AP density curves at different obstacle heights;

[0039] Figure 5 This is a comparison of the coverage probability versus AP density curves at different AP heights. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0041] The wireless network coverage analysis method of the present invention, which considers occlusion effects, as described in this embodiment includes at least the following steps 1 to 3, as shown in the flowchart below. Figure 1 As shown.

[0042] Step 1: Construct a communication network system model for an industrial IoT scenario. This communication network system model includes obstacles, access points, and network communication devices.

[0043] In one example, the communication network system model for building an industrial IoT scenario includes:

[0044] A circular region with radius R, centered on a typical device, is constructed as an industrial IoT scenario. Within this scenario, the horizontal positions of access points (APs) and obstacles both follow a two-dimensional Poisson process, with densities denoted as λ. AP and λ B The horizontal positions of other network devices follow a Poisson point distribution; the height of a typical device is denoted as h0, and the height difference between a typical device and the access point is denoted as a constant Δh; each obstacle is modeled as a cube, and the average length and average width of all cubes are denoted as h0. and Highly obedient to the mean The exponential distribution;

[0045] The path gain from the i-th access point to a typical device is calculated using the following formula:

[0046]

[0047] In the formula, r represents the horizontal distance between the access point and the typical device. The path gain constant is given at a reference distance d0, where f is the frequency, c is the speed of light, α is the path loss exponent, and G is the path gain constant. i The channel gain is caused by small-scale effects and follows a gamma distribution; S i This is the power loss factor caused by the occlusion effect. When there is a line-of-sight path between the i-th access point and the typical device, S i =1 indicates no power loss; otherwise, S i =δ, 0≤δ<1;

[0048] Associate a typical device with the access point closest to it in line of sight.

[0049] Step 2: Calculate the network coverage probability of the communication network system model.

[0050] In one example, calculating the network coverage probability of a communication network system model includes the following steps:

[0051] Step 201: Calculate the power P received by a typical device from the associated access point. r The calculation formula is:

[0052]

[0053] In the formula, P t Here, x is the transmit power, x is the horizontal distance from the typical device to the associated access point, G0 is the corresponding small-scale fading, and S0 is the corresponding blocking loss. Since the typical device is associated with the access point at the nearest line of sight, S0 = 1.

[0054] Step 202, calculate the signal-to-interference-plus-noise ratio (SINR). The calculation formula is as follows:

[0055]

[0056] In the formula, It is noise power, I L The interference is from the line-of-sight access point (LOS AP), I N The interference is from a non-line-of-sight access point (NLOS AP);

[0057] Among them, I L and I N The calculation formulas are as follows:

[0058]

[0059] In the formula, and Let S represent the sets of line-of-sight access points and non-line-of-sight access points, respectively; for all line-of-sight access points, S i =1, for all non-line-of-sight access points, S i =δ;

[0060] Step 203, calculate the network coverage probability using the following formula:

[0061]

[0062] In the formula, T is the signal-to-interference-plus-noise ratio threshold, and P... L P(γ>T) is the probability that there is at least one line-of-sight associated point, and P(γ>T) is the probability that the signal-to-interference-plus-noise ratio is greater than the threshold T given that there is at least one line-of-sight associated point. It represents the probability that an obstacle on a horizontal link will obstruct the communication link; parameter parameter ω is the mean channel gain caused by small-scale effects.

[0063] Step 3: Analyze the trend of network coverage probability with access point density under different occlusion losses, obstacle density, obstacle height and access point height.

[0064] In one example, analyzing the trend of network coverage probability with access point density under different occlusion losses, obstacle density, obstacle height, and access point height includes the following steps:

[0065] Step 301: Substitute the system parameter values ​​into the formula for calculating the coverage probability, including the obstacle density λ. B The height difference Δh between the device and the access point, and the average length of the obstacle. Average width average height Path loss exponent α, power loss δ at non-line-of-sight access points, and noise power The signal-to-interference-plus-noise ratio threshold T;

[0066] Step 302: Change the value of a certain parameter within a certain range. The parameters to be changed include the power loss δ of the non-line-of-sight access point and the obstacle density λ. B Average height of obstacles In addition to the height difference Δh between the device and the access point, keeping other parameters constant, calculate the access point density λ for different values ​​of these parameters. AP The coverage probability below;

[0067] Step 303: Analyze the coverage probability as a function of access point density λ under different parameters. AP The changing trend of parameters was analyzed to assess their impact on the coverage probability.

[0068] The following example further illustrates the wireless network coverage analysis method considering occlusion effects described in this invention. The method includes the following steps:

[0069] S1. Consider an IIoT scenario with a radius of 1000 meters and a communication frequency of 5.5 GHz. What is the average length of the obstacles? and width All are 1m high, with an average height of 1m. The height of a typical device is 1m, and the height difference Δh between it and the AP is 9m; the path loss index α is 3, and the signal-to-interference-plus-noise ratio (SINR) threshold T is 1.

[0070] S2. Calculate the coverage probability.

[0071] S3. Substitute the parameter values ​​set in S1 into the coverage probability expression and analyze the trend of coverage probability with AP density under different occlusion loss, obstacle density, obstacle height and AP height.

[0072] The shading loss δ is set to values ​​of 0, 0.2, and 0.4 respectively. For each value, different values ​​of λ are calculated. AP The corresponding coverage probability, such as Figure 2 As shown, the marked points represent the results of Monte Carlo simulations. The analysis results agree very well with the Monte Carlo simulation results, proving the correctness of the derived coverage probability expression. From Figure 2 It can be seen that as the occlusion loss δ increases, the overall coverage probability decreases significantly, while the optimal AP density remains relatively stable. When the occlusion loss increases, NLOS APs will generate stronger interference, thus the overall coverage probability decreases significantly. Although the occlusion loss is different, the distribution of obstacles remains unchanged, and the AP occlusion situation is similar. Therefore, the optimal AP density is almost the same under different occlusion losses.

[0073] Set the obstacle density λ respectively B The value is 0.05m. -2 0.15m -2 0.25m -2 For each value, calculate different values ​​of λ. AP The corresponding coverage probability, such as Figure 3 As shown in Figures (a) and (b), the relationship between coverage probability and AP density under different obstacle densities is illustrated when δ = 0 and δ = 0.4, respectively. When δ = 0, the maximum coverage probability decreases slightly with increasing obstacle density, and the optimal AP density increases accordingly. In this case, only LOS APs will cause interference because signals from NLOS APs are completely blocked. The increase in optimal AP density can be attributed to the decrease in LOS probability, making it more difficult for APs to maintain LOS connectivity. Therefore, a higher AP density is required to maintain a relatively high SINR level. Furthermore, the slight decrease in maximum coverage probability can be seen as a result of a trade-off between increasing optimal AP density and increasing obstacle density.

[0074] A similar trend emerged when δ = 0.4. Compared to δ = 0, the overall coverage probability decreased significantly. The maximum coverage probability decreased more dramatically with increasing obstacle density, while the increase in optimal AP density was less pronounced. In this case, NLOS APs also generated non-negligible interference, leading to a significant decrease in the overall coverage probability compared to the case where NLOS APs did not generate interference. Simultaneously, when the NLOS AP signal was not completely blocked, the reduction in interference acting on typical devices was less significant with increasing obstruction probability than when δ = 0, resulting in a more pronounced decrease in the maximum coverage probability. Furthermore, higher AP density simultaneously brought stronger signals and more abundant interference sources, making the increase in optimal AP density less significant than when δ = 0.

[0075] Set the average height of the obstacles respectively The values ​​of λ are 3m, 6m, and 9m. Calculate the different values ​​of λ for each value. AP The corresponding coverage probability, such as Figure 4 As shown in the figures, Figures (a) and (b) illustrate the relationship between coverage probability and AP density at different obstacle heights when δ = 0 and δ = 0.4, respectively. For these two cases, the trends are similar to... Figure 3 The similarity is because both increasing obstacle density and increasing obstacle height lead to a greater probability of occlusion, thus having a similar impact on coverage probability.

[0076] The height difference Δh between the device and the AP is set to 2m, 5m, and 8m respectively. For each value, different values ​​of λ are calculated. AP The corresponding coverage probability, such as Figure 5 As shown in Figures (a) and (b), the relationship between coverage probability and AP density at different AP heights is illustrated when δ = 0 and δ = 0.4, respectively. Since the increase in obstacle height and AP height has opposite effects on occlusion probability, the trends in these two cases are similar to... Figure 4 The opposite is true. It is also worth noting that the number of interference sources varies with different AP densities, leading to a trade-off between signal and interference, and consequently, different optimal AP heights.

Claims

1. A method for wireless network coverage analysis considering occlusion effects, characterized in that, include: A communication network system model for an industrial IoT scenario is constructed, which includes obstacles, access points, and network communication devices; the specific process is as follows: Construct a system with a typical device as the origin and a radius of [missing information]. R The circular region is used as an industrial IoT scenario. Within this scenario, the horizontal positions of the access points and obstacles both follow a two-dimensional Poisson process, and their densities are denoted as follows: and The horizontal positions of other network devices follow a Poisson point distribution; the height of a typical device is denoted as... The height difference between a typical device and an access point is denoted as a constant. Each obstacle is modeled as a cube, and the average length and average width of all cubes are denoted as . and Highly observable of the mean The exponential distribution; Calculate the first i The path gain from an access point to a typical device is calculated using the following formula: In the formula, This indicates the horizontal distance between the access point and a typical device. At reference distance The path gain constant is below. It's frequency. c It's the speed of light. It is the path loss index. The channel gain is caused by small-scale effects and follows a gamma distribution; It is the power loss factor caused by the blocking effect, when the first When there is a line-of-sight path between an access point and a typical device This indicates no power loss; otherwise... , ; Associate a typical device with the access point closest to it in line of sight; Calculate the network coverage probability of the communication network system model; the specific process is as follows: Calculate the power received by a typical device from its associated access point. The calculation formula is: In the formula, It's the transmission power. This is the typical horizontal distance from the device to the associated access point. This corresponds to small-scale fading. This corresponds to the occlusion loss, since a typical device is associated with the access point at the nearest line of sight. ; The signal-to-interference-plus-noise ratio (SIR) is calculated using the following formula: In the formula, It is noise power. It's interference from the line-of-sight access point. The interference is from non-line-of-sight access points; in, and The calculation formulas are as follows: In the formula, and Let these represent the sets of line-of-sight access points and non-line-of-sight access points, respectively; for all line-of-sight access points, For all non-line-of-sight access points, ; The formula for calculating the network coverage probability is as follows: In the formula, It is the signal-to-interference-plus-noise ratio threshold. It is the probability that there is at least one line-of-sight related point. The signal-to-interference-plus-noise ratio (SIR) is greater than a threshold provided that there is at least one line-of-sight correlated point. The probability of; It represents the probability that an obstacle on a horizontal link will obstruct the communication link; parameter ,parameter , It is the mean of the channel gain caused by small-scale effects; The variation trend of network coverage probability with access point density under different occlusion losses, obstacle density, obstacle height, and access point height is analyzed.

2. The wireless network coverage analysis method considering occlusion effects according to claim 1, characterized in that, The analysis of the variation trend of network coverage probability with access point density under different occlusion losses, obstacle density, obstacle height, and access point height includes: Step 301: Substitute the system parameter values ​​into the formula for calculating the coverage probability, including obstacle density. Height difference between equipment and access point Average length of obstacles Average width ,average height Path loss index Power loss at non-line-of-sight access points Noise power Signal-to-interference-plus-noise ratio threshold ; Step 302: Change the value of a certain parameter within a certain range. The parameter to be changed includes the power loss of the non-line-of-sight access point. Obstacle density Average height of obstacles and the height difference between the device and the access point Keeping the other parameters constant, calculate the density of different access points for different values ​​of these parameters. The coverage probability below; Step 303: Analyze the coverage probability as a function of access point density under different parameters. The changing trend of parameters was analyzed to assess their impact on the coverage probability.

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