Wireless communication anti-interference protection decision-making method based on discrete particle swarm optimization

Abstract

The invention belongs to the technical field of wireless communication, and discloses a wireless communication anti-interference protection decision-making method based on a discrete particle swarm algorithm. Comprising the following steps: acquiring comprehensive communication data of a wireless communication network; based on the comprehensive communication data, modeling is carried out on a topological structure of the wireless communication network, the wireless communication network is divided into a plurality of grid areas, and each grid area comprises one or a plurality of communication nodes; calculating an interference intensity value in each grid region, and grading the grid regions according to the interference intensity values; obtaining grid regions with different interference levels; carrying out resource allocation on the communication nodes by using a discrete particle swarm algorithm in the grid regions with different interference levels to obtain a resource allocation result; according to the resource allocation result, the working parameters of the communication nodes are dynamically adjusted, anti-interference protection is achieved, and the stability, reliability and performance of the wireless communication network in a complex interference environment are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and more specifically, to a wireless communication anti-interference protection decision-making method based on a discrete particle swarm algorithm. Background Art

[0002] A patent application with the publication number CN117835415A discloses a wireless communication terminal anti-interference method and system, including: each terminal is assigned a terminal identification code after accessing the network and before data communication, and a main channel and a standby channel resource pool are specified; each terminal senses the states of each channel in real time, and records the terminal identification codes of other terminals in the environment, the channels used by each terminal, and the channels being subjected to interference attacks during each data transmission; each terminal independently adopts a given Q-learning algorithm to model the behavior of interferers, predict interference channels, and finally select channels according to the above information; if the main channel is not subject to interference, the main channel is selected for the next data transmission, otherwise, cyclic channel allocation calculations are performed based on its own terminal identification code, the predicted interference channel, and the standby channel resource pool, effectively realizing anti-interference in multiple terminal scenarios, with low computational complexity, no need for information interaction between terminals, and easy to implement.

[0003] However, in a complex urban environment where various interference sources are intertwined, such as high-rise reflections, mobile device interference, industrial equipment electromagnetic radiation, etc., traditional methods can often only deal with a single type of interference and are difficult to comprehensively evaluate and handle such a complex interference scenario; in large-scale events or emergencies, the network environment may change rapidly, and the existing fixed resource allocation strategy is difficult to adjust in time to adapt to this dynamic change, resulting in a sharp decline in network performance; for wireless networks covering a vast area, such as intercity communication systems, the existing technologies are inefficient in processing a large number of nodes and are difficult to achieve real-time optimization, affecting the overall response speed of the network; in addition, in mountainous, hilly and other areas with complex terrains, the traditional coverage range model is too simplified to accurately reflect the actual communication situation, resulting in deviations in network planning and resource allocation; in scenarios with high-density deployments, such as commercial centers or large public places, the existing network topology modeling methods are difficult to take into account both the spatial structure and communication characteristics at the same time, affecting the accuracy of interference assessment and network optimization.

[0004] In view of this, the present invention proposes a wireless communication anti-interference protection decision-making method based on a discrete particle swarm algorithm to solve the above problems. Summary of the Invention

[0005] To overcome the above-mentioned defects of the prior art and to achieve the above object, the present invention provides the following technical solution: A wireless communication anti-interference protection decision-making method based on a discrete particle swarm algorithm, including: Step 1, obtaining comprehensive communication data of a wireless communication network;

[0006] Step 2: Based on the comprehensive communication data, model the topological structure of the wireless communication network, divide the wireless communication network into several grid regions, and each grid region contains one or several communication nodes;

[0007] Step 3: Calculate the interference intensity value within each grid region, and classify the grid regions according to the interference intensity value; obtain grid regions with different interference levels;

[0008] Step 4: In the grid regions with different interference levels, use the discrete particle swarm algorithm to perform resource allocation for the communication nodes to obtain the resource allocation result; according to the resource allocation result, dynamically adjust the working parameters of the communication nodes.

[0009] Furthermore, the comprehensive communication data includes the geographical coordinates of the communication nodes, the coverage range of the communication nodes, the quality information of the communication links, the positions of the interference sources, and the intensities of the interference sources; the quality information of the communication links includes signal strength, signal-to-noise ratio, frame error rate, delay, and throughput.

[0010] Furthermore, the method for obtaining the coverage range of the communication nodes includes:

[0011] Create an empty event queue, and define the starting position and ending position of the scan line; for each communication node, generate a circular coverage area according to its geographical coordinates and theoretical coverage radius; take the left and right boundaries of each circular coverage area as events and add them to the event queue; establish a two-dimensional coordinate system in the space where the circular coverage area is located, and sort the event queue according to the abscissa of the two-dimensional coordinate system;

[0012] Initialize a set, denoted as the active set; move the scan line from left to right, process each event, when the scan line encounters an event corresponding to the left boundary, add the corresponding circular coverage area to the active set, and when it encounters an event corresponding to the right boundary, remove the corresponding circular coverage area from the active set;

[0013] Define the event point as the position where the scan line stops for calculation. At each event point, calculate the intersections of the scan line with all circular coverage areas in the active set, and construct a polygon according to the calculated intersections to represent the preliminary actual coverage range; simplify the preliminary actual coverage range to obtain the final coverage range.

[0014] Furthermore, the method for simplifying the preliminary actual coverage range includes:

[0015] A polygon is a closed figure formed by connecting a series of vertices. The straight line connecting the first vertex and the last vertex of the polygon is denoted as the head-tail connection line; the first vertex is the head point, and the last vertex is the tail point; obtain the vertices of the polygon corresponding to the preliminary actual coverage range and form a sequence, denoted as the vertex sequence; set the dynamic simplification threshold ε = εbase×(1 + β×S)×(1 - γ×I); where, εbase is the basic simplification threshold, S is the local shape complexity factor, I is the semantic importance factor; β and γ are weight coefficients;

[0016] Local shape complexity factor Where, δ is the adjustment coefficient, σ_d is the standard deviation of the distances from the vertices of the local segment to the head-tail connection line, and the local segment is one or several segments of the pre-set polygon; dmean is the average distance from the vertices of the entire polygon to its head-tail connection line;

[0017] Where, SIlocal is the average semantic importance score of the current local segment; SImin is the minimum semantic importance score of the vertices in the entire polygon, and SImax is the maximum semantic importance score of the vertices in the entire polygon; calculate the local curvature and semantic importance score for each vertex;

[0018] Perform weighted calculation on the local curvature and semantic importance scores of each vertex to obtain the comprehensive score of each vertex; find the vertex in the polygon that is farthest from the head-tail connection line. If the comprehensive score of this vertex is higher than the simplification threshold, retain this vertex; remove the vertices with comprehensive scores less than the simplification threshold; recursively perform the same processing on the two segments from the head point to this point and from this point to the tail point; connect the retained vertices into a closed figure to obtain the simplified polygon; apply smoothing processing using the Chai kin algorithm or B-spline curve to the simplified polygon to obtain the final coverage range corresponding to the communication node.

[0019] Furthermore, the calculation formula for the semantic importance score is:

[0020] Where, w1, w2, and w3 are the weights of each weighted term in the formula; d is the distance from the currently calculated vertex to the nearest key communication facility, dr is the reference distance; k is the parameter for adjusting the steepness of the curve, dm is the preset maximum effective distance, AI is the score of the boundary feature point of the coverage area, do is the distance from the currently calculated vertex to the nearest obstacle, and dom is the preset maximum consideration distance;

[0021] Score of the boundary feature point of the coverage area In the formula, θ is the angle formed between the currently calculated vertex and its adjacent point, is the derivative of the angle with respect to the position, and x is the index of the position.

[0022] Furthermore, the method for modeling the topology of the wireless communication network includes:

[0023] Determine the geographical range covered by the entire wireless communication network, evenly divide the area of the entire geographical range into an N×N grid, where N is the size of the grid, and assign a unique identifier to each grid;

[0024] Traverse all communication nodes, and according to the geographical coordinates of the communication nodes, assign them to the corresponding grids, and record the number of nodes and the node list contained in each grid; Define a reorganization rule, and the reorganization rule is: obtain the reorganization index of each grid, and the reorganization index is the weighted sum of the density of communication nodes, average signal-to-noise ratio, average signal strength, average frame error rate, average throughput, and average delay within the grid; Define a splitting threshold and a merging threshold; If the reorganization index of any grid is greater than the splitting threshold, then split it into four grids, and if the largest reorganization index among four adjacent grids is less than the merging threshold, then merge these four adjacent grids into one grid;

[0025] Traverse all grids, gradually apply the reorganization rule, and after each application of the reorganization rule, update the node assignment of the grids until a predetermined number of iterations is reached; At this time, each grid is a lattice area.

[0026] Furthermore, the calculation method of the interference intensity value includes:

[0027] Define interference types, and the interference types include co-channel interference, adjacent-channel interference, and external interference sources; And set the weight coefficients of each interference type;

[0028] Traverse all pairs of communication nodes within the lattice area, and obtain the distance between the communication nodes. Calculate the signal strength attenuation according to the distance and the transmission power of the communication nodes; Calculate the received signal strength PRX = PTX - FSPL + GTX + GRX based on the signal strength attenuation; where PTX is the transmission power, GTX is the transmitting antenna gain, GRX is the receiving antenna gain, and FSPL is the signal strength attenuation;

[0029] Evaluate the co-channel interference degree value SIR based on the received signal strength;

[0030] where i is the index of the interference source, n is the total number of interference sources, I_i is the received power of the i-th interference source, and N0 is the background noise power;

[0031] Calculate the adjacent channel interference degree value corresponding to adjacent channel interference; obtain the interference degree value of a known external interference source; record it as the external interference degree value; multiply the co-channel interference degree value, the adjacent channel interference degree value, and the external interference degree value by the weight coefficients corresponding to the interference types and accumulate them to obtain the overall interference intensity of the corresponding communication node; calculate the average interference intensity of the corresponding grid area based on the overall interference intensity of the communication node, which is the interference intensity value of the corresponding grid area.

[0032] Further, the calculation method of the adjacent channel interference degree value includes:

[0033] Traverse the paired communication nodes u and v using adjacent frequency bands, and obtain the center frequencies f_u and f_v of the communication nodes u and v; obtain the distance d_u,v between the communication nodes u and v; and calculate the path loss FPL. Where, α1 is the adjacent channel interference attenuation coefficient.

[0034] Calculate the adjacent channel received signal strength PX = P_u - FPL + G_u + G_v; where, P_u is the transmit power of the communication node u, G_u is the antenna gain of the communication node u, and G_v is the antenna gain of the communication node v.

[0035] Calculate the adjacent channel interference degree value based on the adjacent channel received signal strength Where, N1 is the receiver noise floor; Ad is the predefined acceptable adjacent channel interference ratio threshold.

[0036] Further, the method for grading the grid area includes:

[0037] Set up a two-dimensional coordinate system in the space where the grid area is located, and assign a unique coordinate to each grid area based on the two-dimensional coordinate system; the coordinate includes the abscissa and the ordinate; convert the abscissa and ordinate of each grid area into binary, interleave the binary of the abscissa and ordinate to form a new binary number; convert this new binary number back to decimal, and use the decimal as the coordinate point of the two-dimensional coordinate system and as the index of the grid area in the two-dimensional coordinate system.

[0038] Sort all grid regions according to the index of the grid region to obtain the sorted grid region sequence; set several interference intensity thresholds for dividing different interference levels; traverse the sorted grid region sequence, read the interference intensity value of the grid region, calculate the weighted average of the interference intensity values of the grid region and its k adjacent grid regions on the two-dimensional coordinate system to obtain the smoothed interference intensity value; based on the set interference intensity threshold, use the smoothed interference intensity value to determine the interference level of the current grid region and associate the grid region with its interference level; all coordinate points corresponding to decimals form a curve; define a window with a fixed size, slide along the curve, and calculate the statistical characteristics of the interference intensity values of the grid regions corresponding to the coordinate points within each window; the statistical characteristics include mean, standard deviation, skewness, and kurtosis; perform a weighted sum on the statistical characteristics to obtain a level index; preset a level threshold, and if the level index within the corresponding window is less than the level threshold, synchronize all grid regions within the window to the same interference level.

[0039] Further, the method for resource allocation to communication nodes includes:

[0040] Define the particle swarm size N2, the maximum number of iterations, and the inertia weight w, the learning factors c1 and c2, create particles for the communication nodes within each grid region, and the particles include two dimensions: channel allocation and power control; randomly initialize the position and velocity of each particle;

[0041] Define the fitness function where a1, a2, a3, a4 are the balance weights of each item and their sum is 1; GRP is the interference level of the grid region, XL is the channel utilization rate of the communication node, PE is the power efficiency of the communication node, and NT is the throughput of the communication node per unit time;

[0042] For each particle corresponding to a grid region, calculate the value of the fitness function of the particle, denoted as fitness; and update the optimal position pB of the particle and the global optimal position gB; update the velocity and position of the particle based on the optimal position pB of the particle and the global optimal position gB: the update formula for velocity is: v(t + 1) = w × v(t) + c1 × rand() × (pB - x(t)) + c2 × rand() × (gB - x(t)); where, v(t + 1) is the velocity of the particle at the (t + 1)-th iteration, v(t) is the velocity of the particle at the t-th iteration; rand() is a random number belonging to the interval (0, 1],

[0043] The update formula for position is:

[0044] x(t + 1) = x(t) + v(t + 1); where, x(t + 1) is the position of the particle at the (t + 1)-th iteration, x(t) is the position of the particle at the t-th iteration;

[0045] After each iteration, perform local search on the globally optimal position using algorithms such as simulated annealing or tabu search; until the preset maximum number of iterations is reached; extract the globally optimal position of the last iteration;

[0046] According to the globally optimal position at this time, determine the channel allocation and power control of each communication node; which is the resource allocation result.

[0047] The technical effects and advantages of a wireless communication anti-interference protection decision method based on a discrete particle swarm algorithm in the present invention:

[0048] The present invention improves the performance and reliability of wireless communication networks in complex environments, significantly enhances the anti-interference ability and adaptability of the network; can comprehensively evaluate the comprehensive impact of various interference factors, greatly improving the accuracy and comprehensiveness of interference analysis; by dynamically optimizing the resource allocation strategy, significantly improving the network resource utilization efficiency, effectively reducing the negative impact of interference on communication quality; in addition, greatly improving the processing efficiency of large-scale complex networks, making real-time optimization possible, thus significantly enhancing the network's response speed and flexibility, and at the same time greatly improving the accuracy of network topology analysis, providing a more reliable basis for subsequent network planning and optimization; generally speaking, enhancing the stability, reliability and performance of wireless communication networks in complex interference environments. Brief Description of the Drawings

[0049] Figure 1 It is a schematic diagram of a wireless communication anti-interference protection decision method based on a discrete particle swarm algorithm of the present invention;

[0050] Figure 2 It is a schematic diagram of a wireless communication anti-interference protection decision system based on a discrete particle swarm algorithm of the present invention. Detailed Embodiments

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1

[0053] Please refer to Figure 1 As shown, a wireless communication anti-interference protection decision method based on a discrete particle swarm algorithm in this embodiment includes:

[0054] Step 1, obtain the comprehensive communication data of the wireless communication network;

[0055] Step 2: Based on the comprehensive communication data, model the topological structure of the wireless communication network, divide the wireless communication network into several grid regions, and each grid region contains one or several communication nodes;

[0056] Step 3: Calculate the interference intensity value within each grid region, and classify the grid regions according to the interference intensity value; obtain grid regions with different interference levels;

[0057] Step 4: In grid regions with different interference levels, use the discrete particle swarm algorithm to allocate resources to communication nodes to obtain the resource allocation result; according to the resource allocation result, dynamically adjust the working parameters of communication nodes to achieve anti-interference protection.

[0058] The comprehensive communication data includes the geographical coordinates of communication nodes, the coverage range of communication nodes, the quality information of communication links, the location of interference sources, and the intensity of interference sources; the quality information of communication links includes signal strength, signal-to-noise ratio, frame error rate, delay, and throughput.

[0059] By setting up a receiver and a transmitter, the receiver measures the signal strength, bit error rate, etc. of the received signal and feeds it back to the transmitter; network devices (such as base stations, routers, etc.) monitor the delay, throughput, etc. of the link and report them to the network management system; use link test tools (such as Ping, Iperf, etc.) to actively test and sample the link.

[0060] The acquisition method of the coverage range of communication nodes includes:

[0061] Create an empty event queue, and define the starting position and ending position of the scan line; for each communication node, generate a circular coverage area according to its geographical coordinates and theoretical coverage radius; the theoretical coverage radius is the maximum reachable distance calculated according to the transmit power, antenna gain, and path loss; add the left and right boundaries of each circular coverage area as events to the event queue; establish a two-dimensional coordinate system in the space where the circular coverage area is located, and sort the event queue according to the abscissa of the two-dimensional coordinate system.

[0062] Initialize a set, denoted as the active set; move the scan line from left to right, process each event, when the scan line encounters an event corresponding to the left boundary, add the corresponding circular coverage area to the active set, and when it encounters an event corresponding to the right boundary, remove the corresponding circular coverage area from the active set.

[0063] Define the event point as the position where the scan line stops for calculation. At each event point, calculate the intersections of the scan line with the covered areas of all circles in the active set. Based on the calculated intersections, construct a polygon to represent the preliminary actual coverage range; simplify the preliminary actual coverage range to obtain the final coverage range. Specifically, a polygon is a closed figure formed by connecting a series of vertices. The straight line connecting the first vertex and the last vertex of the polygon is denoted as the head-tail connection line; the first vertex is the head point, and the last vertex is the tail point.

[0064] Obtain the vertices of the polygon corresponding to the preliminary actual coverage range and form a sequence, denoted as the vertex sequence; set the dynamic simplification threshold ε = εbase×(1 + β×S)×(1 - γ×I); where, εbase is the basic simplification threshold (a fixed value), S is the local shape complexity factor, I is the semantic importance factor; β and γ are weight coefficients used to adjust the influence degree of each factor.

[0065] Local shape complexity factor Among them, δ is the adjustment coefficient that controls the sensitivity of the influence of shape complexity, σ_d is the standard deviation of the distance from the vertices of the local segment to the head-tail connection line, and the local segment is one or several segments of the polygon set in advance; dmean is the average distance from the vertices of the entire polygon to its head-tail connection line.

[0066] Among them, SIlocal is the average semantic importance score of the current local segment; SImin is the minimum semantic importance score of the vertices in the entire polygon, SImax is the maximum semantic importance score of the vertices in the entire polygon; calculate the local curvature (calculate the curvature using the three-point method) and semantic importance score for each vertex; the calculation formula for the semantic importance score is:

[0067] Among them, w1, w2, and w3 are the weights of each weighted term in the formula; d is the distance from the currently calculated vertex to the nearest key communication facility, dr is the reference distance, and its value is a small positive number to balance the influence weights of the key communication facility on nearby and distant vertices; k is the parameter that adjusts the steepness of the curve, dm is the preset maximum effective distance, AI is the score of the boundary feature point of the coverage area, do is the distance from the currently calculated vertex to the nearest obstacle, and dom is the preset maximum consideration distance.

[0068] Score of the boundary feature point of the coverage area In the formula, θ is the angle formed between the currently calculated vertex and its adjacent point, is the derivative of the angle with respect to the position, understood as the angle difference between adjacent two points divided by the distance between them, and x is the index of the position.

[0069] The local curvature and semantic importance score of each vertex are weighted and calculated to obtain the comprehensive score of each vertex; find the vertex in the polygon that is farthest from the line connecting the head and the tail. If the comprehensive score of this vertex is higher than the simplification threshold, then retain this vertex; remove the vertices with comprehensive scores less than the simplification threshold; recursively perform the same processing on the two segments from the head point to this point and from this point to the tail point; connect the retained vertices into a closed figure to obtain the simplified polygon; apply smoothing processing using the Chaikin algorithm or B-spline curve to the simplified polygon to obtain the final coverage range of the corresponding communication node.

[0070] In the case of a large number of nodes and a complex coverage area, it can effectively handle the overlap and intersection of the coverage area, provide more accurate coverage range information, and provide a better basis for subsequent interference evaluation and resource allocation.

[0071] The ways to model the topology of a wireless communication network include:

[0072] Determine the geographical range covered by the entire wireless communication network, evenly divide the area of the entire geographical range into an N×N grid, where N is the size of the grid, and assign a unique identifier to each grid; traverse all communication nodes, and according to the geographical coordinates of the communication nodes, assign them to the corresponding grids, and record the number of nodes and the node list contained in each grid; define a reorganization rule, and the reorganization rule is: obtain the reorganization index of each grid, and the reorganization index is the weighted sum of the density, average signal-to-noise ratio, average signal strength, average frame error rate, average throughput, and average delay of the communication nodes in the grid; define a splitting threshold and a merging threshold.

[0073] If the reorganization index of any grid is greater than the splitting threshold, then split it into four grids. If the largest reorganization index among the adjacent four grids is less than the merging threshold, then merge these adjacent four grids into one grid; traverse all grids, gradually apply the reorganization rule, and after each application of the reorganization rule, update the node allocation of the grids until a predetermined number of iterations is reached; at this time, each grid is a lattice area; obtain a multi-scale, dynamic wireless communication network topology model; not only captures the spatial structure of the network, but also contains rich communication characteristic information; provides a powerful basis for subsequent network analysis, optimization, and management, and can effectively handle large-scale, complex wireless communication networks.

[0074] Define the types of interference, including co-channel interference, adjacent-channel interference, and external interference sources; and set the weight coefficients for each type of interference. Traverse all pairs of communication nodes within the grid area, and obtain the distances between the communication nodes. Calculate the signal strength attenuation based on the distances and the transmit powers of the communication nodes, and use the Free Space Path Loss model to calculate the signal strength attenuation. Calculate the received signal strength PRX = PTX - FSPL + GTX + GRX based on the signal strength attenuation; where PTX is the transmit power, GTX is the transmit antenna gain, GRX is the receive antenna gain, and FSPL is the signal strength attenuation.

[0075] Evaluate the co-channel interference degree value SIR based on the received signal strength.

[0076] where i is the index of the interference source, n is the total number of interference sources, I_i is the received power (linear unit) of the i-th interference source, and N0 is the background noise power (linear unit); the background noise power refers to the sum of all other unwanted electromagnetic energies in a wireless communication system except for the useful signal and specific interference sources, and is usually estimated by measuring the output of the receiver when there is no signal transmission.

[0077] Calculate the adjacent-channel interference degree value corresponding to adjacent-channel interference; specifically, traverse the pairs of communication nodes u and v using adjacent frequency bands, and obtain the center frequencies f_u and f_v of the communication nodes u and v; obtain the distance d_u,v between the communication nodes u and v; and calculate the path loss FPL.

[0078] where α1 is the adjacent-channel interference attenuation coefficient, with a value between 0 and 1, and is an important parameter for measuring and calculating the degree of adjacent-channel interference.

[0079] Calculate the adjacent-channel received signal strength PX = P_u - FPL + G_u + G_v; where P_u is the transmit power of the communication node u, G_u is the antenna gain of the communication node u, and G_v is the antenna gain of the communication node v; calculate the adjacent-channel interference degree value based on the adjacent-channel received signal strength where N1 is the receiver noise floor (the minimum noise level generated by the receiver itself); Ad is the predefined acceptable adjacent-channel interference ratio threshold.

[0080] Obtain the interference degree value of known external interference sources from the database or real-time monitoring system; denote it as the external interference degree value; multiply the co-channel interference degree value, adjacent-channel interference degree value, and external interference degree value by the weight coefficients corresponding to the interference types and accumulate them to obtain the overall interference intensity of the corresponding communication node; calculate the average interference intensity of the corresponding grid area (the average of the overall interference intensities of the communication nodes within it) based on the overall interference intensity of the communication node; this is the interference intensity value of the corresponding grid area; this process takes into account various interference factors and conducts quantitative analysis, providing a reliable basis for subsequent grading and optimization.

[0081] The methods for grading the grid areas include:

[0082] Set up a two-dimensional coordinate system in the space where the grid area is located, and assign a unique coordinate to each grid area based on the two-dimensional coordinate system; the coordinate includes the abscissa and the ordinate; convert the abscissa and ordinate of each grid area into binary, interleave the binary of the abscissa and ordinate to form a new binary number; convert this new binary number back to decimal, and use the decimal as the coordinate point of the two-dimensional coordinate system and as the index of the grid area in the two-dimensional coordinate system.

[0083] Sort all the grid areas according to the index of the grid area to obtain the sorted grid area sequence; ensure that the grid areas adjacent in space are also as adjacent as possible in the one-dimensional sequence.

[0084] Set several interference intensity thresholds for dividing different interference levels; for example, low interference level: 0 - 30, medium interference level: 31 - 60, high interference level: 61 - 90, extremely high interference level: 91 - 100.

[0085] Traverse the sorted grid area sequence, read the interference intensity value of the grid area, calculate the weighted average of the interference intensity values of the grid area and its k adjacent grid areas (k is a preset parameter) on the two-dimensional coordinate system to obtain the smoothed interference intensity value; based on the set interference intensity thresholds, use the smoothed interference intensity value to determine the interference level of the current grid area, and associate the grid area with its interference level; all the coordinate points corresponding to the decimals form a curve; define a window with a fixed size, slide along the curve, and within each window, calculate the statistical characteristics of the interference intensity values of the grid areas corresponding to the coordinate points; the statistical characteristics include mean, standard deviation, skewness, and kurtosis; perform a weighted sum of the statistical characteristics to obtain the level index; preset a level threshold, if the level index within the corresponding window is less than the level threshold, synchronize all the grid areas within the window to the same interference level.

[0086] Generate a mapping to associate each grid area with its finally determined interference level, and visualize the result, for example, use different colors to mark the grid areas with different interference levels.

[0087] Effectively convert the grid area in the two-dimensional space into a one-dimensional sequence, while largely maintaining the spatial locality. When dealing with large-scale spatial data, it can quickly perform area query and update operations, providing efficient support for anti-interference decision-making in wireless communication networks.

[0088] The ways of resource allocation for communication nodes include:

[0089] Define the particle swarm size N2 (the number of particles), the maximum number of iterations, and the inertia weight w, the learning factors c1 and c2. Initially, set c1 large and c2 small to encourage extensive exploration. Later, set c1 small and c2 large to accelerate convergence; the sum of c1 and c2 is kept around 4; create particles for the communication nodes in each grid area, and each particle contains two dimensions: channel allocation and power control; randomly initialize the position and velocity of each particle;

[0090] Define the fitness function where a1, a2, a3, and a4 are the balance weights of each item, and their sum is 1; GRP is the interference level of the grid area, XL is the channel utilization rate of the communication node, PE is the power efficiency of the communication node (data transmission rate under unit power consumption), and NT is the throughput of the communication node per unit time.

[0091] The channel utilization rate is the average time that the channel of the communication node is occupied within a fixed time range multiplied by the actual data throughput within the fixed time range and divided by the theoretical maximum capacity; for the particle corresponding to each grid area, calculate the value of the fitness function of the particle, denoted as fitness; and update the best position pB of the particle and the global best position gB; the best position for each particle is the best position it has experienced during the search process, and the best position makes the fitness of the corresponding particle the smallest; the global best position is the particle with the smallest fitness among all particles.

[0092] Update the velocity and position of the particle based on the best position pB and the global best position gB of the particle: The update formula for the velocity is:

[0093] v(t + 1) = w × v(t) + c1 × rand() × (pB - x(t)) + c2 × rand() × (gB - x(t)); where, v(t + 1) is the velocity of the particle at the (t + 1)-th iteration, v(t) is the velocity of the particle at the t-th iteration; rand() is a random number belonging to the interval (0, 1];

[0094] The update formula for the position is:

[0095] x(t + 1) = x(t) + v(t + 1); where x(t + 1) is the position of the particle at the (t + 1)-th iteration, and x(t) is the position of the particle at the t-th iteration;

[0096] After each iteration, use algorithms such as simulated annealing or tabu search to perform local search on the global optimal position; until the preset maximum number of iterations is reached; extract the global optimal position of the last iteration;

[0097] According to the global optimal position at this time, determine the channel allocation and power control for each communication node; the global optimal position is usually a multi-dimensional vector, which is decoded into specific resource allocation parameters; for channel allocation, use discrete values or probability values, that is, directly map to specific channels, such as 3 indicating the allocation of the 3rd channel; or use roulette or other probability selection methods to determine the final channel; for nodes competing for the same channel, implement time division multiplexing or other sharing mechanisms, and implement interference coordination among nodes close in space to obtain the resource allocation result; convert the final resource allocation scheme into configuration instructions executable by network devices.

[0098] Through a network management system or other management means, distribute the configuration instructions to each communication node. The configuration instructions can adopt standard configuration languages or protocols, such as CLI, NETCONF, etc.; after receiving the configuration instructions, the communication node parses and executes the corresponding commands; the communication node uses the new channel and power parameters for wireless communication, reducing the impact of co-channel / adjacent-channel interference. Power control helps to reduce interference to other nodes.

[0099] According to the optimization results obtained by the discrete particle swarm algorithm, dynamically adjust key parameters such as the operating frequency and transmission power of each communication node, actively avoid interference, improve communication quality and network performance, so as to achieve adaptive anti-interference protection.

[0100] This embodiment improves the performance and reliability of wireless communication networks in complex environments, significantly enhances the anti-interference ability and adaptability of the network; can comprehensively evaluate the comprehensive impact of various interference factors, greatly improving the accuracy and comprehensiveness of interference analysis; by dynamically optimizing the resource allocation strategy, significantly improving the network resource utilization efficiency, effectively reducing the negative impact of interference on communication quality; in addition, greatly improving the processing efficiency of large-scale complex networks, making real-time optimization possible, thus significantly enhancing the network response speed and flexibility, and at the same time greatly improving the accuracy of network topology analysis, providing a more reliable basis for subsequent network planning and optimization; generally speaking, enhancing the stability, reliability and performance of wireless communication networks in complex interference environments.

[0101] Embodiment 2

[0102] Please refer toFigure 2 As shown, for the parts not described in detail in this embodiment, refer to the description in Embodiment 1. A wireless communication anti-interference protection decision-making system based on the discrete particle swarm algorithm is provided, including:

[0103] A data acquisition module for acquiring comprehensive communication data of the wireless communication network;

[0104] A modeling module that, based on the comprehensive communication data, models the topological structure of the wireless communication network, divides the wireless communication network into several grid regions, and each grid region contains one or several communication nodes;

[0105] A level division module for calculating the interference intensity value within each grid region and classifying the grid regions according to the interference intensity value; obtaining grid regions with different interference levels;

[0106] A dynamic allocation module for, within grid regions with different interference levels, using the discrete particle swarm algorithm to allocate resources to communication nodes to obtain a resource allocation result; dynamically adjusting the working parameters of the communication nodes according to the resource allocation result. Each module is connected by wired and / or wireless means to achieve data transmission between modules.

[0107] Embodiment 3

[0108] This embodiment publicly provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the operation mode of the above-provided wireless communication anti-interference protection decision-making method based on the discrete particle swarm algorithm.

[0109] Since the electronic device introduced in this embodiment is the electronic device used to implement a wireless communication anti-interference protection decision-making method based on the discrete particle swarm algorithm in an embodiment of the present application, based on the wireless communication anti-interference protection decision-making method based on the discrete particle swarm algorithm introduced in an embodiment of the present application, those skilled in the art can understand the specific implementation manners and various variations of the electronic device in this embodiment. Therefore, the specific implementation of how this electronic device implements the method in an embodiment of the present application will not be described in detail here. As long as those skilled in the art implement the electronic device used in a wireless communication anti-interference protection decision-making method based on the discrete particle swarm algorithm in an embodiment of the present application, it falls within the scope of protection of the present application.

[0110] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0111] The above are only the preferred embodiments of the present invention. The protection scope of the present invention is not limited to the above embodiments. All technical solutions falling within the concept of the present invention belong to the protection scope of the present invention. It should be noted that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A wireless communication anti-interference protection decision method based on discrete particle swarm algorithm, characterized in that: include: Step 1, obtaining comprehensive communication data of the wireless communication network; Step 2: Based on the comprehensive communication data, the topological structure of the wireless communication network is modeled, and the wireless communication network is divided into a plurality of grid areas, each grid area containing one or a plurality of communication nodes; Step 3: Calculate the interference intensity value in each grid area, and classify the grid areas according to the interference intensity value to obtain grid areas with different interference levels; Step 4: In grid areas with different interference levels, use the discrete particle swarm algorithm to allocate resources to communication nodes and obtain resource allocation results; according to the resource allocation results, dynamically adjust the working parameters of the communication nodes.

2. According to the discrete particle swarm algorithm based wireless communication anti-interference protection decision method of claim 1, it is characterized in that: The comprehensive communication data includes the geographic coordinates of the communication node, the coverage of the communication node, the quality information of the communication link, the location of the interference source and the strength of the interference source; the quality information of the communication link includes signal strength, signal-to-noise ratio, frame error rate, delay and throughput.

3. According to claim 2, a wireless communication anti-interference protection decision method based on discrete particle swarm algorithm is characterized in that: The coverage range of the communication node is obtained by: Create an empty event queue and define the start and end positions of the scan line; for each communication node, generate a circular coverage area based on its geographic coordinates and theoretical coverage radius; add the left and right boundaries of each circular coverage area as events to the event queue; establish a two-dimensional coordinate system in the space where the circular coverage area is located, and sort the event queue according to the horizontal coordinate of the two-dimensional coordinate system; Initialize a set, record it as the active set; move the scan line from left to right, process each event, when the scan line encounters an event corresponding to the left boundary, add the corresponding circular coverage area to the active set, when encountering an event corresponding to the right boundary, remove the corresponding circular coverage area from the active set; Define the event point as the position where the scan line stops for calculation. At each event point, calculate the intersection of the scan line with all circular coverage areas in the active set. Based on the calculated intersection points, construct a polygon to represent the preliminary actual coverage range; simplify the preliminary actual coverage range to obtain the final coverage range.

4. According to claim 3, a wireless communication anti-interference protection decision method based on discrete particle swarm algorithm is characterized in that: The methods of simplifying the preliminary actual coverage include: A polygon is a closed figure formed by a series of vertices. The straight line connecting the first vertex and the last vertex of the polygon is recorded as the head-to-tail line; the first vertex is the head point, and the last vertex is the tail point; the vertices of the polygon corresponding to the preliminary actual coverage range are obtained and formed into a sequence, which is recorded as the vertex sequence; the dynamic simplification threshold ε=εbase×(1+β×S)×(1-γ×I) is set; where εbase is the basic simplification threshold, S is the local shape complexity factor, and I is the semantic importance factor; β and γ are weight coefficients; Local shape complexity factor Among them, δ is the adjustment coefficient, σ_d is the standard deviation of the distance from the vertex of the local segment to the head and tail connection line, and the local segment is a segment or several segments of a pre-set polygon; dmean is the average value of the distance from the vertex of the entire polygon to its head and tail connection line; Among them, SIlocal is the average semantic importance score of the current local segment; SImin is the minimum semantic importance score of the vertices in the entire polygon, and SImax is the maximum semantic importance score of the vertices in the entire polygon; the local curvature and semantic importance score of each vertex are calculated; The local curvature and semantic importance score of each vertex are weighted to obtain the comprehensive score of each vertex; the vertex farthest from the head-tail line in the polygon is found, and if the comprehensive score of the vertex is higher than the simplification threshold, the vertex is retained; the vertices with a comprehensive score less than the simplification threshold are removed; the two segments from the first point to the point and from the point to the tail point are recursively processed in the same way; the retained vertices are connected into a closed figure to obtain a simplified polygon; the simplified polygon is smoothed using the Chaikin algorithm or the B-spline curve to obtain the final coverage of the corresponding communication node.

5. According to claim 4, a wireless communication anti-interference protection decision method based on discrete particle swarm algorithm is characterized in that: The calculation formula of the semantic importance score is: Among them, w1, w2 and w3 are the weights of each weighted term in the formula; d is the distance from the currently calculated vertex to the nearest key communication facility, dr is the reference distance; k is the parameter for adjusting the steepness of the curve, dm is the preset maximum effective distance, AI is the score of the boundary feature point of the coverage area, do is the distance from the currently calculated vertex to the nearest obstacle, and dom is the preset maximum considered distance; Scores of feature points at the boundary of the coverage area Where θ is the angle between the currently calculated vertex and its adjacent points. is the derivative of the angle with respect to the position, and x is the index of the position.

6. The wireless communication anti-interference protection decision method based on discrete particle swarm algorithm according to claim 5 is characterized in that: The method of modeling the topological structure of the wireless communication network includes: Determine the geographical range covered by the entire wireless communication network, divide the area of ​​the entire geographical range evenly into N×N grids, where N is the size of the grid, and assign a unique identifier to each grid; Traverse all communication nodes, assign them to corresponding grids according to their geographic coordinates, and record the number of nodes and node list contained in each grid; define reorganization rules, which are: obtain the reorganization index of each grid, which is the weighted sum of the density of communication nodes in the grid, average signal-to-noise ratio, average signal strength, average frame error rate, average throughput, and average delay; define split thresholds and merge thresholds; if the reorganization index of any grid is greater than the split threshold, split it into four grids; if the largest reorganization index among the four adjacent grids is less than the merge threshold, merge the four adjacent grids into one grid; Traverse all grids and gradually apply the reorganization rules. After each application of the reorganization rules, update the node allocation of the grid until the predetermined number of iterations is reached; at this time, each grid is a grid area.

7. The wireless communication anti-interference protection decision method based on discrete particle swarm algorithm according to claim 6 is characterized in that: The calculation method of the interference intensity value includes: Define the interference type, which includes co-channel interference, adjacent channel interference and external interference sources; and set the weight coefficient of each interference type; Traverse all pairs of communication nodes in the grid area, obtain the distance between the communication nodes, and calculate the signal strength attenuation based on the distance and the transmission power of the communication node; based on the signal strength attenuation calculation, the received signal strength PRX = PTX-FSPL+GTX+GRX is obtained; where PTX is the transmission power, GTX is the transmission antenna gain, GRX is the receiving antenna gain, and FSPL is the signal strength attenuation; Based on the received signal strength evaluation, the co-channel interference level value SIR is obtained; Where i is the index of the interference source, n is the total number of interference sources, I_i is the received power of the i-th interference source, and N0 is the background noise power; Calculate the adjacent-channel interference degree value corresponding to the adjacent-channel interference; obtain the interference degree value of the known external interference source; record it as the external interference degree value; multiply the same-channel interference degree value, the adjacent-channel interference degree value, the external interference degree value and the weight coefficient of the corresponding interference type and add them together to obtain the overall interference intensity of the corresponding communication node; calculate the average interference intensity of the corresponding grid area based on the overall interference intensity of the communication node, which is the interference intensity value of the corresponding grid area.

8. The wireless communication anti-interference protection decision method based on discrete particle swarm algorithm according to claim 7 is characterized in that: The calculation method of the adjacent channel interference degree value includes: Traverse the pairs of communication nodes u and v using adjacent frequency bands, obtain the center frequencies f_u and f_v of the communication nodes u and v; obtain the distance d_u,v between the communication nodes u and v; and calculate the path loss FPL; Among them, α1 is the adjacent channel interference attenuation coefficient; The adjacent frequency received signal strength PX is calculated as follows: P_u=P_u-FPL+G_u+G_v; wherein P_u is the transmission power of communication node u, G_u is the antenna gain of communication node u, and G_v is the antenna gain of communication node v; Calculate the adjacent channel interference value based on the adjacent channel received signal strength Wherein, N1 is the receiver noise floor; Ad is the predefined acceptable adjacent channel interference ratio threshold.

9. The wireless communication anti-interference protection decision method based on discrete particle swarm algorithm according to claim 8 is characterized in that: The method of grading the grid area includes: A two-dimensional coordinate system is set in the space where the grid area is located, and a unique coordinate is assigned to each grid area based on the two-dimensional coordinate system; the coordinate includes a horizontal coordinate and a vertical coordinate; the horizontal coordinate and the vertical coordinate of each grid area are converted into binary, and the binary of the horizontal coordinate and the vertical coordinate are interleaved to form a new binary number; the new binary number is converted back to decimal, and the decimal is used as a coordinate point of the two-dimensional coordinate system, and as an index of the grid area in the two-dimensional coordinate system; All grid areas are sorted according to the index of the grid area to obtain a sorted grid area sequence; several interference intensity thresholds are set to divide different interference levels; the sorted grid area sequence is traversed, the interference intensity value of the grid area is read, and the weighted average of the interference intensity values ​​of the grid area and its k adjacent grid areas in the two-dimensional coordinate system is calculated to obtain a smoothed interference intensity value; based on the set interference intensity threshold, the interference level of the current grid area is determined using the smoothed interference intensity value, and the grid area is associated with its interference level; all decimal corresponding coordinate points constitute a curve; a fixed-size window is defined, and the curve is slid along the curve, and in each window, the statistical characteristics of the interference intensity value of the grid area corresponding to the coordinate point are calculated; the statistical characteristics include mean, standard deviation, skewness and kurtosis; the statistical characteristics are weighted summed to obtain a level index; a level threshold is preset, and if the level index in the corresponding window is less than the level threshold, all grid areas in the window are synchronized to the same interference level.

10. The wireless communication anti-interference protection decision method based on discrete particle swarm algorithm according to claim 9, characterized in that: The method of allocating resources to the communication nodes includes: Define the particle swarm size N2, the maximum number of iterations and the inertia weight w, the learning factors c1 and c2, create particles for each communication node in the grid area, and the particles contain two dimensions: channel allocation and power control; randomly initialize the position and speed of each particle; Define the fitness function Where a1, a2, a3, and a4 are the balance weights of each item, and their sum is 1; GRP is the interference level of the grid area, XL is the channel utilization of the communication node, PE is the power efficiency of the communication node, and NT is the throughput per unit time of the communication node; For each particle corresponding to the grid area, calculate the value of the particle's fitness function, recorded as fitness; and update the particle's optimal position pB and the global optimal position gB; based on the particle's optimal position pB and the global optimal position gB, update the particle's speed and position: the speed update formula is: v(t+1)=w×v(t)+c1×rand()×(pB-x(t))+c2×rand()×(gB-x(t)); where v(t+1) is the velocity of the particle at the t+1th iteration, v(t) is the velocity of the particle at the tth iteration; rand() is a random number in the interval (0, 1], The update formula of position is: x(t+1)=x(t)+v(t+1); where x(t+1) is the position of the particle at the t+1th iteration, and x(t) is the position of the particle at the t+1th iteration; After each iteration, a local search is performed on the global optimal position using algorithms such as simulated annealing or taboo search until the preset maximum number of iterations is reached; and the global optimal position of the last iteration is extracted; According to the global optimal position at this time, the channel allocation and power control of each communication node are determined; that is, the resource allocation result.

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