A wi-fi 6 network throughput optimization method based on bss coloring and channel allocation

Abstract

The present application relates to a kind of based on BSS dyeing and channel allocation Wi-Fi network throughput optimization method, this method is based on the BSS dyeing mechanism introduced in Wi-Fi6 agreement, in the industrial scene that AP deployment is completed and the association relationship of AP and terminal is known, with network total throughput as optimization goal, from the reduction of wireless medium collision between BSS, according to the conflict relationship between terminal, the additional competition terminal number introduced when BSS uses same channel is judged, channel optimization allocation problem model is established, obtains as high as possible overall throughput in limited frequency spectrum resource, the scheme has better network performance.

Description

Technical Field

[0001] This application relates to the fields of industrial internet and wireless networks, and in particular to a method for optimizing Wi-Fi network throughput based on BSS coloring and channel allocation. Background Technology

[0002] With the rapid development of the Industrial Internet, wireless local area networks (WLANs) have become a widely adopted solution in industrial communication. Wireless access points (APs) are commonly used devices for building small WLANs, offering advantages such as long wireless transmission distances, high reliability, and strong device flexibility. The throughput of Wi-Fi networks directly affects the efficiency of actual industrial production.

[0003] In Wi-Fi networks, communication channels need to be allocated to each access point (AP) for network operation and data transmission. Terminal devices can then scan, authenticate, and connect to their associated APs to transmit data. When adjacent APs use the same channel, they can cause severe interference, significantly reducing network throughput. However, Wi-Fi network channel resources are very limited. With the increasing trend towards large-scale and high-density networks, there is an urgent need for new channel allocation schemes to solve this technical problem and improve network throughput. Summary of the Invention

[0004] The main objective of this application is to provide a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation. The method aims to determine the channels used by each AP under a given spatial model, so as to reduce inter-AP collisions and interference, reuse channel resources as much as possible, and improve the overall network throughput performance.

[0005] To achieve the above objectives, this application provides a method for optimizing Wi-Fi network throughput based on BSS coloring and channel allocation, comprising the following steps:

[0006] Step 1: Obtain information such as obstacle geometry, mobile terminal path, fixed terminal location, and the association status between dynamic and static terminals and the AP;

[0007] Step 2: Based on the spatial model and the association status of terminals and APs, establish an optimization problem model;

[0008] Step 3: Based on the optimization problem model, decompose it into two sub-problems: perform BSS coloring on each AP and allocate channels to the colored APs, and establish sub-optimization problem models for each.

[0009] Step 4: Execute the heuristic BSS coloring algorithm;

[0010] Step 5: After completing the BSS coloring, execute the channel allocation algorithm based on BSS coloring, allocate non-overlapping channels among BSSs of the same color, and change the allocation order of non-overlapping channels within each BSS color.

[0011] In step 1, the acquired obstacle geometry information, mobile terminal path information, and fixed terminal location information are the three-dimensional spatial information of the AP to which the channel needs to be allocated. The acquired dynamic and static terminal association status information with the AP is used to measure the impact of wireless medium contention between terminals in the overlapping channel BSS on throughput.

[0012] In step 2, the optimization problem model is as follows:

[0013]

[0014]

[0015] The optimization objective is to minimize the number of terminals experiencing wireless medium contention across all time slices, thereby maximizing the throughput of terminals in contention states. The constraint is that each access point (AP) is allocated a channel, where N... a Indicates the number of APs, l i t represents the channel number assigned to the i-th AP. n Indicates the total number of time slices. This represents the throughput at time slice k. This represents the number of terminals participating in the competition for the i-th BSS after considering conflicting terminals, i.e.

[0016]

[0017] Where, N s N represents the number of fixed terminals. m Indicates the number of mobile terminals. Let Δn′ be the expression for the number of terminals associated with the i-th AP at time slice k. ij,k In the k-th time slice, when the i-th BSS and the j-th BSS use the same channel but have different colors, the i-th BSS introduces an additional number of competing terminals for the j-th BSS.

[0018] In step 3, the sub-problems are: performing BSS coloring on each AP and allocating channels to the colored APs. The model for the first sub-optimization problem is as follows:

[0019]

[0020]

[0021] The BSS color c assigned to each AP is the optimization variable. The optimization objective is to maximize the total throughput loss among APs within BSSs of the same color. The number of BSSs colored by each color does not exceed N. c As a constraint, N c N represents the number of available channels. a N represents the number of APs. BSS b is the total number of colors used to color the BSS. ij Indicates whether the BSS number corresponding to the j-th AP is i. This represents the throughput loss caused by the i-th BSS using the same channel and the same color to the j-th BSS. The second sub-problem, the optimization objective, is to find a channel allocation scheme that minimizes the conflict between BSSs of different colors, which can be directly transformed into the overall optimization problem of this invention, i.e., the original optimization model.

[0022] In step 4, the heuristic BSS staining algorithm includes the following steps:

[0023] Step 4-1: Randomly generate an initial solution. During the generation of the initial solution, use Na / Nc colors to color the BSS, and try to make the number of BSSs colored by each color as large as possible.

[0024] Step 4-2: Initialize the random search population by copying the initial solution from Step 4-1 Np times as the initial values ​​for the solution population;

[0025] Step 4-3: Perform a random search. For each individual in the search population, randomly select two APs and swap the colors of their corresponding BSSs;

[0026] Step 4-4: Calculate the objective function value of each individual in the solution population. If the objective function value of an individual is better than the global optimal function value, then update the global optimal solution to that individual.

[0027] Steps 4-5: Update all individuals in the search population to the global optimal solution;

[0028] Step 4-6: Continue with step 4-3;

[0029] Steps 4-7: When the number of iterations reaches the set maximum number of iterations, stop the iteration and output the current global optimal solution as the algorithm result.

[0030] In step 4, the heuristic BSS coloring algorithm, compared to traditional algorithms such as the Monte Carlo method which are based on statistical experiments, solves the problem faster and can obtain an approximate optimal solution in a shorter time. Compared to other heuristic collision avoidance algorithms, this heuristic BSS coloring algorithm utilizes the BSS coloring mechanism of the IEEE 802.11ax PHY layer, which can be implemented directly at the physical layer. It can quickly determine the color of the source BSS without parsing the packet, resulting in higher efficiency and better algorithm performance.

[0031] In step 4, the purpose of BSS coloring is to mark the BSSs corresponding to a group of physically close and overlapping APs with the same color, and to allocate non-overlapping channels to each AP within the BSS, so as to reduce or avoid channel medium collisions between overlapping BSSs. Step 4 is a BSS coloring method aimed at maximizing collisions within BSSs of the same color, and uses a heuristic algorithm to quickly find the optimal BSS coloring scheme.

[0032] In step 5, after BSS coloring is completed, non-overlapping channels are allocated among BSSs of the same color to avoid conflicts between BSSs of the same color. Furthermore, by changing the allocation order of non-overlapping channels within each BSS color, conflicts between BSSs are minimized, thereby achieving the highest total throughput. Based on the BSS coloring scheme obtained in step 4, and with the goal of maximizing conflicts between BSSs of different colors, a heuristic algorithm is used to quickly find the optimal channel allocation scheme. Step 5, the channel allocation algorithm based on BSS coloring, includes the following steps:

[0033] Step 5-1: Initialize the random search group. Randomly assign non-overlapping channels to the APs corresponding to each color BSS in a search individual, and duplicate this individual multiple times as a random search group.

[0034] Step 5-2: Perform a random iterative search, traverse each individual in the solution group, and then traverse the BSS of each color, and randomly redistribute the channel order within the BSS of that color with probability p.

[0035] Step 5-3: Calculate the objective function value of each individual in the solution group. If the objective function value of an individual is better than the global optimum, then update the global optimum to that individual.

[0036] Step 5-4: Update each individual solver in the solver group to the global optimum;

[0037] Step 5-5: Continue the random search in Step 5-2 until the maximum number of iterations is reached, then stop solving the problem and output the global optimal solution as the result of the algorithm.

[0038] In step 5, the channel allocation algorithm based on BSS coloring, compared to traditional algorithms such as the Monte Carlo method and other statistically experimental methods, solves the problem faster and can obtain an approximate optimal solution in a shorter time. Compared to other heuristic algorithms, the channel allocation algorithm based on BSS coloring, utilizing the BSS coloring mechanism and dynamic CCA threshold, achieves better solution speed with the same number of iterations. Furthermore, the algorithm allocates channels with probability p, effectively preventing it from getting trapped in local optima, a situation common in other heuristic algorithms. In addition, this algorithm based on the dynamic CCA threshold exhibits better adaptability than other traditional, intelligent, and heuristic algorithms, demonstrating excellent performance under various conditions.

[0039] Compared to existing technologies, the advantages of this invention are as follows: This application provides a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation, establishing a unified channel optimization allocation problem model considering both mobile and fixed terminals and oriented towards total network throughput. Compared to traditional Wi-Fi network throughput optimization techniques, this invention proposes a channel optimization allocation method based on BSS coloring through a dynamic CCA threshold mechanism and a BSS coloring mechanism. Therefore, the channel allocation results obtained by this invention achieve overall system optimization for both mobile and fixed terminals, rather than static optimization at individual time slices, resulting in better network throughput performance compared to traditional methods such as Monte Carlo algorithms and GCA algorithms. Furthermore, the algorithm proposed in this invention utilizes the BSS coloring mechanism of the IEEE 802.11ax PHY layer, quickly determining the color of the source BSS without parsing packets. It increases the threshold for detecting wireless medium collisions at the device's physical layer through a dynamic CCA threshold, and achieves spatial multiplexing of the channel by reducing the number of device backoffs, resulting in higher efficiency and better algorithm performance. Furthermore, the algorithm proposed in this invention has better adaptability compared to other traditional algorithms, intelligent algorithms, and heuristic algorithms. It can exhibit excellent algorithm performance under different conditions and prevent the occurrence of local optima, and is very close to the theoretical optimal value obtained through exhaustive search. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0041] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0042] Figure 1This is a flowchart illustrating the first embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention.

[0043] Figure 2 This is a schematic diagram of the heuristic BSS coloring algorithm in the first embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention.

[0044] Figure 3 This is a schematic diagram of the specific process of the channel allocation algorithm based on BSS coloring in the first embodiment of the Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention.

[0045] Figure 4 This is a second embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention. It compares the allocation quality and computation time with the ideal value, GCA algorithm and Monte Carlo algorithm under different fixed terminal numbers in small and medium-sized networks.

[0046] Figure 5 This is a third embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention. It compares the allocation quality and computation time with the ideal value, GCA algorithm and Monte Carlo algorithm under different numbers of mobile terminals in small and medium-sized networks.

[0047] Figure 6 This is the fourth embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention. It compares the allocation quality and computation time with the ideal value, GCA algorithm and Monte Carlo algorithm under different numbers of APs in small and medium-sized networks.

[0048] Figure 7 This is the fifth embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention. It compares the allocation quality and computation time with the ideal value, GCA algorithm and Monte Carlo algorithm under different channel numbers in small and medium-sized networks.

[0049] Figure 8 This is the sixth embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention. It compares the allocation quality and computation time with the GCA algorithm and Monte Carlo algorithm under different numbers of APs in a large-scale network.

[0050] Figure 9This is the seventh embodiment of a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention. It compares the allocation quality and computation time with the GCA algorithm and Monte Carlo algorithm under different channel numbers in a large-scale network. Detailed Implementation

[0051] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.

[0052] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of this application.

[0053] Example: The first embodiment of the present invention provides a method for optimizing Wi-Fi network throughput based on BSS coloring and channel allocation, such as... Figure 1 As shown in the embodiment of the Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention, the Wi-Fi network throughput optimization method based on BSS coloring and channel allocation includes the following steps:

[0054] Step S10: Obtain information such as the three-dimensional environment, obstacle geometry, mobile terminal path, fixed terminal position, and the association status between dynamic and static terminals and the AP.

[0055] In this embodiment, it should be noted that obtaining the environmental information in three-dimensional space includes the x-axis, y-axis, and z-axis information, which is the distribution space of the wireless APs (e.g., if the size of the three-dimensional space is 50m × 50m × 10m, then the size in the x-axis direction is 50m, the size in the y-axis direction is 50m, and the size in the z-axis direction is 10m). After performing grid discretization processing on this three-dimensional space, the location information of obstacles, fixed terminals, and trajectory information of mobile terminals can be obtained. At the same time, executing this embodiment requires obtaining the association relationship between fixed terminals and mobile terminals and APs in advance, that is, which terminals each AP establishes a connection with.

[0056] Step S20: Based on the spatial model and the association status of the terminal and AP, establish an optimization problem model;

[0057] In this embodiment, it should be noted that this step measures the impact of wireless medium contention between terminals in BSSs using overlapping channels on throughput by calculating the additional number of competing terminals introduced when adjacent BSSs select the same channel as the current BSS. This establishes the optimization problem model in this step, with the optimization objective being to minimize the number of terminals generating wireless medium contention in all time slices, thereby maximizing the throughput of terminals in contention.

[0058] Step S30: Based on the optimization problem model, the problem is decomposed into two sub-problems: BSS coloring is performed on each AP and channels are allocated to the colored APs. Sub-optimization problem models are established for each AP.

[0059] In this embodiment, it should be noted that for the first sub-problem, the optimization objective is to find a BSS coloring scheme that maximizes the collision within BSSs of the same color, thereby increasing the throughput gain of allocating non-overlapping channels to APs within BSSs of the same color. This is achieved by introducing the i-th BSS with the same channel and color, which causes throughput loss to the j-th BSS. The first sub-optimization problem model is established; for the second sub-problem, the optimization objective is to find a channel allocation scheme that minimizes the conflict between different colored BSSs, and the optimization model can be directly converted into the overall optimization problem model of this invention.

[0060] Step S40: Execute the heuristic BSS coloring algorithm;

[0061] In this embodiment, it should be noted that this step uses a heuristic algorithm to solve the problem. In order to improve the efficiency of the solution, a large-scale optimization is performed using a solution population, and the individual with the highest objective function is selected for the next iteration.

[0062] Step S50: After completing BSS coloring, execute the channel allocation algorithm based on BSS coloring, allocate non-overlapping channels among BSSs of the same color, and change the allocation order of non-overlapping channels within each BSS color.

[0063] The first embodiment of this invention provides a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation, wherein the heuristic BSS coloring algorithm is as follows: Figure 2 As shown in the embodiment of the Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention, the heuristic BSS coloring algorithm includes the following steps:

[0064] Step A10: Randomly generate an initial solution. During the generation of the initial solution, the BSS is colored using Na / Nc colors, and the number of BSSs colored by each color is maximized.

[0065] Step A20: Initialize the random search population. Make Np copies of the initial solution from Step 1, using these copies as the initial values ​​for the solution population.

[0066] Step A30: Perform a random search. For each individual in the search population, randomly select two APs and swap the colors of their corresponding BSSs;

[0067] Step A40: Calculate the objective function value for each individual in the solution population. If the objective function value of an individual is better than the global optimal value, then update the global optimal solution to that individual.

[0068] Step A50: Update all individuals in the search population to the global optimum.

[0069] Step A60, continue to step A30;

[0070] Step A70: When the number of iterations reaches the set maximum number of iterations, stop the iteration and output the current global optimal solution as the algorithm result.

[0071] The first embodiment of this invention provides a Wi-Fi network throughput optimization method based on BSS coloring and channel allocation, wherein the channel allocation algorithm based on BSS coloring is as follows: Figure 3 As shown in the embodiment of the Wi-Fi network throughput optimization method based on BSS coloring and channel allocation proposed in this invention, the channel allocation algorithm based on BSS coloring includes the following steps:

[0072] Step B10: Initialize the random search population. Randomly assign non-overlapping channels to the APs corresponding to each color BSS in a search individual, and duplicate this individual multiple times to form a random search population;

[0073] Step B20: Perform a random iterative search. Iterate through each solver in the solver group, and then iterate through the BSSs of each color, randomly redistributing the channel order within the BSS of that color with probability p.

[0074] Step B30: Calculate the objective function value of each individual in the solution group. If the objective function value of an individual is better than the global optimum, then update the global optimum to that individual.

[0075] Step B40: Update each individual solver in the solver group to the global optimum;

[0076] Step B50: Continue the random search in step two until the maximum number of iterations is reached, then stop solving the problem and output the global optimal solution as the result of the algorithm.

[0077] This application employs the aforementioned scheme, namely, acquiring obstacle geometry information, mobile terminal paths, fixed terminal locations, and the association status between dynamic and static terminals and access points (APs). Then, based on the spatial model and the association status between terminals and APs, an optimization problem model is established and decomposed into two sub-problems: BSS coloring for each AP and channel allocation for the colored APs, with separate sub-optimization problem models for each. A heuristic BSS coloring algorithm is then executed. After BSS coloring, a channel allocation algorithm based on BSS coloring is implemented, allocating non-overlapping channels among BSSs of the same color and changing the allocation order of non-overlapping channels within each BSS color. Compared to traditional channel allocation schemes, this algorithm has lower computational complexity and can achieve the highest possible overall throughput with limited spectrum resources.

[0078] Furthermore, in the specific second embodiment of this application, considering a small-to-medium scale network, four mobile terminals (two transport robots and two patrol robots respectively), and eight access points (APs), multiple experiments were conducted with different fixed terminal numbers. The AP transmit power was 20 dBm, the terminal transmit power was 6 dBm, the CCA protocol threshold between BSSs of different colors was -72 dB, and the CCA protocol threshold between BSSs of the same color was -82 dB. (Refer to...) Figure 4 .

[0079] In the specific third embodiment of this application, considering a small-to-medium-scale network, 80 fixed terminals, and 8 access points (APs), multiple experiments were conducted with different numbers of mobile terminals. The number of transport robots and patrol robots was equal. The AP transmit power was 20 dBm, the terminal transmit power was 6 dBm, the CCA protocol threshold between BSSs of different colors was -72 dB, and the CCA protocol threshold between BSSs of the same color was -82 dB. (Refer to...) Figure 5 .

[0080] In the specific fourth embodiment of this application, considering a small-to-medium-scale network, 80 fixed terminals, and 4 mobile terminals (2 transport robots and 2 patrol robots respectively), multiple experiments were conducted with different numbers of APs. The AP transmit power was 20 dBm, the terminal transmit power was 6 dBm, the CCA protocol threshold between BSSs of different colors was -72 dB, and the CCA protocol threshold between BSSs of the same color was -82 dB. (Refer to...) Figure 6 .

[0081] In the second, third, and fourth embodiments, it should be noted that, as can be seen from the experimental results, in the above three embodiments, the objective function value of the algorithm proposed in this invention is much higher than that of the GCA algorithm and Monte Carlo algorithm using dynamic CCA threshold, and is almost equal to the ideal value when using dynamic CCA threshold, which proves the effectiveness of the algorithm proposed in this chapter.

[0082] Furthermore, in the specific fifth embodiment of this application, considering a small-to-medium-scale network, 80 fixed terminals, 4 mobile terminals (2 transport robots and 2 patrol robots respectively), and 8 APs, multiple experiments were conducted under different channel numbers. The AP transmit power was 20dBm, the terminal transmit power was 6dBm, the CCA protocol threshold between BSSs of different colors was -72dB, and the CCA protocol threshold between BSSs of the same color was -82dB. (Refer to...) Figure 7 .

[0083] In the fifth embodiment, it should be noted that the experimental results show that as the number of available channels increases, more and more BSSs in the network can be allocated to non-overlapping channels, thus reducing wireless media collisions in the network. Therefore, the objective function values ​​of various methods are improved. Moreover, the objective function value of the algorithm in this chapter is higher than that of the GCA algorithm and Monte Carlo algorithm using dynamic CCA threshold under various available channel numbers, and is basically equal to the ideal value when using dynamic CCA threshold, and much higher than the ideal value when not using dynamic CCA threshold, proving the effectiveness of the algorithm proposed in this chapter under different available channel numbers.

[0084] Furthermore, in the sixth specific embodiment of this application, considering a large-scale network, 200 fixed terminals, 10 mobile terminals (5 transport robots and 5 patrol robots respectively), and 3 available channels, multiple experiments were conducted with different numbers of APs. The AP transmit power was 20dBm, the terminal transmit power was 6dBm, the CCA protocol threshold between BSSs of different colors was -72dB, and the CCA protocol threshold between BSSs of the same color was -82dB. (Refer to...) Figure 8 .

[0085] In the specific seventh embodiment of this application, considering a large-scale network, 200 fixed terminals, 10 mobile terminals (5 transport robots and 5 patrol robots respectively), and 40 access points (APs), multiple experiments were conducted under different channel numbers. The AP transmit power was 20 dBm, the terminal transmit power was 6 dBm, the CCA protocol threshold between BSSs of different colors was -72 dB, and the CCA protocol threshold between BSSs of the same color was -82 dB. (Refer to...) Figure 9 .

[0086] The experimental results of the proposed algorithm in large-scale networks are basically consistent with those in small-to-medium-scale networks. In experiments with small-to-medium-scale networks, the proposed algorithm has a longer computation time than the Monte Carlo algorithm. However, in large-scale networks, the algorithm required is significantly shorter than the Monte Carlo algorithm. This is because the proposed algorithm decomposes the channel allocation problem into a BSS coloring problem and a BSS-based channel allocation problem, thus transforming the more complex problem into two less complex subproblems, greatly improving the speed of solving the subproblems.

[0087] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for optimizing Wi-Fi network throughput based on BSS coloring and channel allocation, characterized in that, The optimization method includes the following steps: Step 1: Obtain obstacle geometry information, mobile terminal path, fixed terminal location, and association status information between dynamic and static terminals and the AP; Step 2: Based on the spatial model and the association status of terminals and APs, establish an optimization problem model; Step 3: Based on the optimization problem model, decompose it into two sub-problems: perform BSS coloring on each AP and allocate channels to the colored APs, and establish sub-optimization problem models for each. Step 4: Execute the heuristic BSS coloring algorithm; Step 5: After completing BSS coloring, execute the channel allocation algorithm based on BSS coloring, allocate non-overlapping channels among BSSs of the same color, and change the allocation order of non-overlapping channels within each BSS color. In step 4, the heuristic BSS staining algorithm includes the following steps: Step 4-1: Randomly generate an initial solution. During the generation of the initial solution, use Na / Nc colors to color the BSS, and try to make the number of BSSs colored by each color as large as possible. Step 4-2: Initialize the random search population by copying the initial solution from Step 4-1 Np times as the initial values ​​for the solution population; Step 4-3: Perform a random search. For each individual in the search population, randomly select two APs and swap the colors of their corresponding BSSs. Step 4-4: Calculate the objective function value of each individual in the solution population. If the objective function value of an individual is better than the global optimal function value, then update the global optimal solution to that individual. Steps 4-5: Update all individuals in the search population to the global optimal solution; Step 4-6: Continue with step 4-3; Steps 4-7: When the number of iterations reaches the set maximum number of iterations, stop the iteration and output the current global optimal solution as the algorithm result.

2. The Wi-Fi network throughput optimization method based on BSS coloring and channel allocation as described in claim 1, characterized in that, In step 1, the acquired obstacle geometry information, mobile terminal path information, and fixed terminal location information are the three-dimensional spatial information of the AP to which channel allocation is required. The acquired dynamic and static terminal association status information with the AP is used to assess the impact of wireless medium contention between terminals using the overlapping channel BSS on throughput.

3. The Wi-Fi network throughput optimization method based on BSS coloring and channel allocation as described in claim 1, characterized in that, In step 2, the optimization problem model is as follows: The optimization objective is to minimize the number of terminals experiencing wireless medium contention across all time slices, thereby maximizing the throughput of terminals in contention states. The constraint is that each access point (AP) is allocated a channel. Indicates the number of APs. This represents the channel number assigned to the i-th AP. This indicates the total number of time slices. This represents the throughput at time slice k. This represents the number of terminals participating in the competition for the i-th BSS after considering conflicting terminals, i.e. in, Indicates the number of fixed terminals. Indicates the number of mobile terminals. Let k be the expression for the number of terminals associated with the i-th AP at time slice k. In the k-th time slice, when the i-th BSS and the j-th BSS use the same channel but have different colors, the i-th BSS introduces an additional number of competing terminals for the j-th BSS.

4. The Wi-Fi network throughput optimization method based on BSS coloring and channel allocation as described in claim 1, characterized in that, In step 3, the sub-problems are: performing BSS coloring on each AP and allocating channels to the colored APs. The model for the first sub-optimization problem is as follows: The BSS color c assigned to each AP is the optimization variable. The optimization objective is to maximize the total throughput loss among APs within BSSs of the same color. The number of BSSs colored by each color shall not exceed a certain limit. As constraints, The number of available channels. For the number of APs, The total number of colors used to color the BSS. Indicates whether the BSS number corresponding to the j-th AP is i. The first subproblem represents the throughput loss caused by the i-th BSS using the same channel and the same color to the j-th BSS. The second subproblem is to find a channel allocation scheme that minimizes the conflict between BSSs of different colors. This can be directly converted into the overall optimization problem of this invention, i.e., the original optimization model.

5. The Wi-Fi network throughput optimization method based on BSS coloring and channel allocation as described in claim 1, characterized in that, In step 5, after completing the BSS coloring, non-overlapping channels are allocated among BSSs of the same color to avoid conflicts between BSSs of the same color. By changing the allocation order of non-overlapping channels within each BSS color, the conflicts between BSSs are minimized, thereby obtaining the highest total throughput. Based on the BSS coloring scheme obtained in step 4, with the goal of maximizing the conflicts between BSSs of different colors, the optimal channel allocation scheme is quickly found through a heuristic algorithm.

6. The Wi-Fi network throughput optimization method based on BSS coloring and channel allocation as described in claim 1, characterized in that, Step 5, the channel allocation algorithm based on BSS coloring includes the following steps: Step 5-1: Initialize the random search group. Randomly assign non-overlapping channels to the APs corresponding to each color BSS in a search individual, and duplicate this individual multiple times as a random search group. Step 5-2: Perform a random iterative search, traverse each individual in the solution group, and then traverse the BSS of each color, and randomly redistribute the channel order within the BSS of that color with probability p. Step 5-3: Calculate the objective function value of each individual in the solution group. If the objective function value of an individual is better than the global optimum, then update the global optimum to that individual. Step 5-4: Update each individual solver in the solver group to the global optimum; Step 5-5: Continue the random search in Step 5-2 until the maximum number of iterations is reached, then stop solving the problem and output the global optimal solution as the result of the algorithm.

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