Distributed communication interference resource scheduling method

Abstract

The invention discloses a distributed communication interference resource scheduling method, which comprises the following steps of: dividing a distributed population into a master population and slave populations, completing initialization in a roulette manner, independently running each slave population, and carrying out interlace operation of local variation and full replication; during interference initialization, the interference power is adjusted to the maximum gear, sub-population interference parts meeting preset constraint conditions are screened out, and sub-population fitness is calculated; dynamically adjusting power scheduling and the number of jammers through the fitness values of the master population and the slave population; and realizing comprehensive interference suppression on the communication network through a preset multi-objective optimization function. The invention provides a resource scheduling algorithm with scene constraint and a target function, introduces the number of jammers and a power scheduling strategy, optimizes deployment, prolongs communication interference working time, can save interference resources, can quickly search an optimal interference scheme, and shows good convergence performance and application potential.

Description

Technical Field

[0001] The present invention relates to the field of distributed interference communication, and in particular to a distributed communication interference resource scheduling method. Background Art

[0002] Communication jamming is a technology that interferes with enemy communication links by blocking or suppressing them, with the goal of blocking or blinding enemy command and control links, thereby creating a local asymmetric advantage on the battlefield. Artificial intelligence technology has been widely used in various fields of modern battlefields, improving the feasibility of anti-jamming technologies such as communication resource scheduling and jamming identification. However, the popularity of drone deep attacks has reduced the effectiveness of traditional long-distance high-power jamming. Faced with dense radiation sources and complex damage threat environments, distributed communication jamming can provide a variety of response strategies in time and space through flexible and effective objective functions and intelligent scheduling optimization algorithms.

[0003] At present, interference resource scheduling is a multi-constrained, multi-parameter non-convex non-deterministic polynomial problem, and there is no optimal solution. Existing research mainly focuses on modeling and algorithm design, and proposes a variety of interference resource allocation models and optimization methods for different application scenarios. However, these methods often lack the pertinence to specific needs of communication interference, such as interference suppression effect, system working time, and the distance between the jammer and communication equipment. In addition, the existing communication interference power scheduling algorithm is mainly aimed at traditional high-power centralized interference equipment, and fails to fully consider the factors such as the large number of jammers, low power, and dispersed site selection in distributed multi-point and multi-target scenarios, and the pertinence of the scheduling of the number of jammers is weak.

[0004] In view of the shortcomings of the existing mathematical model objective functions and algorithms, the present invention proposes a distributed communication interference resource scheduling method based on a master-slave parallel genetic algorithm. The method establishes a distributed communication interference scenario and resource allocation model, proposes two objective functions, and comprehensively considers the interference suppression effect, system working time, the number of jammers and the spacing of communication nodes to achieve the interference method with the greatest comprehensive benefit. In view of the shortcomings of the standard genetic algorithm in scheduling the number of jammers and interference power, the present invention improves the master-slave parallel genetic algorithm, introduces the number of jammers and power scheduling strategy, and significantly improves the adaptability, convergence speed and scheduling efficiency of the algorithm. The simulation experimental results show that the algorithm and objective function proposed in the present invention can save interference resources and quickly search for the optimal interference method in simulating the construction of low-power distributed communication interference scenarios, showing good convergence performance and practical application potential. Summary of the invention

[0005] The object of the present invention is to provide a distributed communication interference resource scheduling method to at least solve the problem of the insufficiency of the existing mathematical model objective function and algorithm in the interference resource scheduling.

[0006] In order to solve the above technical problems, the present invention provides a distributed communication interference resource scheduling method, comprising:

[0007] The distributed population is divided into a master population and a slave population, wherein the master population is responsible for updating the global optimal solution and the slave population is responsible for independent evolution;

[0008] Initialization is completed in a roulette manner, and each of the slave populations runs independently to perform local mutation and full replication crossover operations;

[0009] When the communication interference is initialized, the interference power is adjusted to the maximum level, the sub-population interference deployment that meets the preset constraints is selected, and the sub-population fitness is calculated;

[0010] Dynamically adjust power scheduling and the number of jammers through the fitness values ​​of the master population and the slave population;

[0011] Comprehensive interference suppression of the communication network is achieved through a preset multi-objective optimization function.

[0012] Optionally, in the distributed communication interference resource scheduling method, the distributed population is divided into a master population and a slave population, and initialization is completed in a roulette manner, and each slave population runs independently to perform a crossover operation of local mutation and full replication, including:

[0013] The diversity of individuals in the population is determined by using a local mutation method to perform mutation operations on individuals in the population and a full replication crossover method.

[0014] The master population is generated by cross-pollination of the historically optimal slave populations to guide the power scheduling of the slave populations;

[0015] After the master population is updated, it will be immediately crossed to all slave populations, and each of the slave populations will perform mutation and crossover operations independently.

[0016] Optionally, in the distributed communication interference resource scheduling method, dynamically adjusting power scheduling and the number of jammers by using the fitness values ​​of the master population and the slave population includes:

[0017] Determining a power scheduling strategy according to the fitness values ​​of the master population and the slave population;

[0018] According to the fitness value of the slave population, the probability of power increase or power decrease is adjusted to optimize the power scheduling strategy;

[0019] After updating the fitness value, the master population transmits the new power scheduling strategy to each slave population through the communication mechanism:

[0020] Dynamically monitor the execution of the power scheduling strategy and dynamically adjust the interference signal power and interference location based on real-time data to ensure effective coverage of the interference signal and maximize the interference effectiveness.

[0021] Optionally, in the distributed communication interference resource scheduling method, the dynamically adjusting the power scheduling and the number of jammers by the fitness values ​​of the master population and the slave population further includes:

[0022] By generating random numbers, the number and location of jammers can be dynamically adjusted;

[0023] According to a preset interference environment, a basic probability value for scheduling the number of jammers is set to control the increase and decrease of the number of jammers;

[0024] When the fitness value of the main population is not updated, triggering the jammer number scheduling operation;

[0025] Through multiple jammer number scheduling, the distribution of the number of jammers is determined to maximize the jamming effectiveness.

[0026] Optionally, in the distributed communication interference resource scheduling method, the multi-objective optimization function is used to balance the interference signal power, the number of communication nodes and the number of jammers to balance the interference effectiveness and resource utilization efficiency, and the multi-objective optimization function F is:

[0027]

[0028] The multi-objective optimization function includes a global interference effectiveness coefficient E, a system working time coefficient T, a spatial aggregation penalty coefficient S and a quantity penalty coefficient Q;

[0029] Among them, a1, a2, a3, and a4 are weighted coefficients, representing the importance of each indicator to the optimization of distributed communication interference deployment and power scheduling, and can be adjusted according to various anti-interference requirements;

[0030] The preset objective function F1 is used to focus on the deployment of a large number of low-power jammers;

[0031] The preset objective function 2 F2 is used to strengthen the restriction on the number of jammers and minimize the deployment of jamming resources;

[0032] The global interference effectiveness coefficient E is a weighted step interference-to-signal ratio that maximizes the global interference effectiveness in distributed communication interference;

[0033] The system working time coefficient T is used to measure the continuous working time of the jammer battery;

[0034] The spatial aggregation penalty coefficient S is used to ensure that the jammers are distributed as dispersed as possible;

[0035] The quantity penalty coefficient Q is used to balance the total number of jammers and the jamming power to meet different anti-jamming requirements.

[0036] Optionally, in the distributed communication interference resource scheduling method, the comprehensive interference suppression of the communication network through a preset multi-objective optimization function includes:

[0037] According to different anti-interference requirements, dynamically adjust the weighted weights of the multi-objective optimization function for the interference signal power, the number of communication nodes and the number of jammers; wherein the weighted convergence of the objective function is adjusted by a scaling factor;

[0038] The value of the multi-objective optimization function is calculated in real time, and the value corresponding to the global optimal interference is searched through continuous iteration.

[0039] Optionally, in the distributed communication interference resource scheduling method, before the distributed population is divided into a master population and a slave population, the method further includes:

[0040] Analyze the distribution and number of communication nodes in a distributed communication network;

[0041] Determine the initial location and number of jammers;

[0042] Establish constraints on interference resources.

[0043] Optionally, in the distributed communication interference resource scheduling method, the initialization is completed in a roulette manner, further comprising:

[0044] By setting the algorithm parameters, including the maximum number of iterations, the number of chromosome sets, the maximum number of available jammers, and the number of UHF radio stations;

[0045] Generate initial chromosomes in the main population, each chromosome represents a possible interference scheme;

[0046] Generate auxiliary chromosomes from the population for local search and fine-tuning;

[0047] The interference effect of the newly generated population individuals is evaluated according to the preset fitness function, the population individuals with higher fitness are retained in the main population, and the population individuals with lower fitness are eliminated.

[0048] Optionally, in the distributed communication interference resource scheduling method, the spatial aggregation penalty coefficient S is specifically:

[0049]

[0050] Among them, A3 is the convergence scaling factor of the weighted objective function, x i ,y iis the horizontal and vertical coordinates of jammer i, is the horizontal and vertical coordinates of the jammer i2, r min is the penalty distance between jammers, in meters.

[0051] Optionally, in the distributed communication interference resource scheduling method, the quantity penalty coefficient Q is specifically:

[0052]

[0053] Among them, A4 is the convergence scaling factor of the weighted objective function, M is the number of enemy UHF communication nodes, and N is the number of jammers used.

[0054] The beneficial effects of the embodiments of the present invention are as follows: by establishing a distributed communication interference scenario and a resource allocation model, adaptive scheduling requirements for a variety of environments are achieved, and the multi-objective optimization function comprehensively considers the interference suppression effect, system working time, number of jammers and communication node spacing, and in addition, the interference signal power, number of communication nodes and number of jammers are weighted by weight to achieve comprehensive interference suppression of the communication network system, and the global optimal interference scheme can be searched through continuous iteration to ensure the maximization of system performance. The present invention also proposes a jammer number and power scheduling strategy, and improves the master-slave parallel genetic algorithm to improve the convergence performance and efficiency of the algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0056] Figure 1 A flow chart of an embodiment of a distributed communication interference resource scheduling method of the present application;

[0057] Figure 2 A schematic diagram of the structure of an embodiment of a distributed communication interference resource scheduling device of the present application;

[0058] FIG3( a ) is a schematic diagram of simulating a communication node deployment one in a specific embodiment of the present application;

[0059] FIG3( b ) is a schematic diagram of simulating communication node deployment 2 in a specific embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to enable those skilled in the art to better understand the method of the present invention, the embodiments of the present invention will be described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0061] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0062] At present, the mathematical models and solution algorithms that have been studied are not very targeted for the scheduling of distributed communication interference resources. In the existing electronic countermeasures field, there are many studies in the field of radar interference, but few studies in communication interference, and insufficient attention is paid to the scheduling needs of different scenarios for instantaneous full-range suppression of ultra-short wave fixed-frequency communication networks. In addition, a fixed number of high-power jammers are often used in interference scenarios, and the construction of the objective function does not include factors such as the system working time, the distance between the jammer and the communication equipment, and there is little consideration for the scheduling of the number of jammers. In addition, the existing communication interference power scheduling algorithm is mainly aimed at traditional high-power centralized interference equipment, focusing more on the selection of interference power and interference frequency bands. It still plays a role in the scenario of fixed jammers targeting a few targets, but in the scenario of "multi-point-multi-purpose", factors such as the large number of jammers, low power, and dispersed site selection are not considered, and the scheduling of the number of jammers is not very targeted. Furthermore, in the simulation experiments of existing studies, most of the comparisons are based on a certain interference indicator and a certain algorithm performance, and there is a lack of comparison of different objective functions and the complete generation process of interference schemes.

[0063] In view of the defects of the objective function and algorithm of the above mathematical model, the embodiments of the present invention are applied to interference resource scheduling in distributed communications to meet the needs of intelligent and diversified battlefield games.

[0064] See also Figure 1 , Figure 1 FIG. 1 is a basic flow chart of the distributed communication interference resource scheduling method of this embodiment. Figure 1 As shown, the steps include:

[0065] S1100, dividing the distributed population into a master population and a slave population, wherein the master population is responsible for updating the global optimal solution, and the slave population is responsible for independent evolution;

[0066] S1200, completing initialization in a roulette manner, each of the slave populations independently operates to perform local mutation and full replication crossover operations;

[0067] S1300, when the communication interference is initialized, the interference power is adjusted to the maximum gear, the sub-population interference deployment that meets the preset constraint conditions is screened out, and the sub-population fitness is calculated;

[0068] S1400, dynamically adjusting power scheduling and the number of jammers according to the fitness values ​​of the master population and the slave population;

[0069] S1500. Comprehensive interference suppression of the communication network is achieved through a preset multi-objective optimization function.

[0070] Since the SGA (Standard Genetic Algorithm) algorithm can imitate the random optimization search method of biological evolution in nature. The principle is to search for excellent genes and guide the evolution of the population through operations such as encoding, selection, crossover, and mutation, and then obtain the optimal solution. SGA is simple and easy to implement, and can be widely used in fields such as combinatorial optimization, function optimization, and machine learning. However, it has weak scheduling capabilities for the number of distributed communication interferences and power resources, and takes a long time. It has poor adaptability to the requirements of different interference scenarios, and the algorithm is not robust in scenarios with a small number of jammers. The Master-Slave Parallel Genetic Algorithm (MSPGA) is a method that divides the population into a master population and a slave population, and runs them in parallel according to different functions. In the embodiment of the present invention, improvements are made to the master-slave parallel genetic algorithm MSPGA.

[0071] In the embodiment of the present invention, in S1100-S1200, the distributed population is divided into a master population and a slave population, and the individuals are encoded in a real number encoding method to simplify the complexity of encoding and decoding. Further, the distributed population is divided into a master population and a slave population, and the initialization is completed in a roulette manner, and each of the slave populations runs independently, performing a crossover operation of local mutation and full replication, including:

[0072] The diversity of individuals in the population is determined by using a local mutation method to perform mutation operations on individuals in the population and a full replication crossover method.

[0073] The master population is generated by cross-pollination of the historically optimal slave populations to guide the power scheduling of the slave populations;

[0074] After the master population is updated, it will be immediately crossed to all slave populations, and each of the slave populations will perform mutation and crossover operations independently.

[0075] In the specific implementation, each individual in the population represents a candidate solution for the interference scheme, and the initialization is completed in a roulette manner. The mutation method is local mutation, and the crossover method is full replication. Each slave population runs independently, mutates and evolves, and does not cross with other slave populations; the only master population is generated by the crossover of the historically optimal slave population, guiding the power scheduling of the sub-population. After the master population is updated, it will be immediately crossed to all sub-populations.

[0076] When the main population and the slave population are initialized in a roulette manner, the algorithm parameters are set, including the maximum number of iterations, the number of chromosome groups, the maximum number of available jammers, and the number of ultra-short wave radio stations; initial chromosomes are generated in the main population, each chromosome represents a possible jamming scheme; auxiliary chromosomes are generated in the slave population for local search and fine-tuning. The jamming effect of the newly generated individuals is further evaluated according to the fitness function, and the individuals with higher fitness are retained in the main population, while the individuals with lower fitness are eliminated.

[0077] As described in S1300 to S1400, when the communication interference is initialized, the interference power is adjusted to the maximum level, the sub-population interference deployment that meets the preset constraints is screened out, and the sub-population fitness is calculated; the power scheduling and the number of jammers are dynamically adjusted through the fitness values ​​of the main population and the slave population.

[0078] Furthermore, the dynamically adjusting power scheduling and the number of jammers by using the fitness values ​​of the master population and the slave population includes:

[0079] Determining a power scheduling strategy according to the fitness values ​​of the master population and the slave population;

[0080] According to the fitness value of the slave population, the probability of power increase or power decrease is adjusted to optimize the power scheduling strategy;

[0081] After updating the fitness value, the master population transmits the new power scheduling strategy to each slave population through the communication mechanism:

[0082] Dynamically monitor the execution of the power scheduling strategy and dynamically adjust the interference signal power and interference location based on real-time data to ensure effective coverage of the interference signal and maximize the interference effectiveness.

[0083] In specific implementation, the present invention solves the problem that the current genetic algorithm cannot efficiently adjust the coupling relationship between power and fitness function under constrained conditions. The present invention innovatively proposes two power scheduling strategies. The first power scheduling strategy is a scheduling algorithm based on variable power, as shown in the following table:

[0084]

[0085]

[0086] The second power scheduling strategy is a fixed-position power scheduling algorithm, as shown in the following table:

[0087]

[0088] Furthermore, since one chromosome of the genetic algorithm represents one candidate solution, only a fixed number of jammer solutions can be optimized, so the present invention improves the design of a jammer number search scheduling strategy. Specifically, the power scheduling and the number of jammers are dynamically adjusted through the fitness values ​​of the master population and the slave population, and also includes:

[0089] By generating random numbers, the number and location of jammers can be dynamically adjusted;

[0090] According to a preset interference environment, a basic probability value for scheduling the number of jammers is set to control the increase and decrease of the number of jammers;

[0091] When the fitness value of the main population is not updated, triggering the jammer number scheduling operation;

[0092] Through multiple jammer number scheduling, the distribution of the number of jammers is determined to maximize the jamming effectiveness.

[0093] In specific implementation, the present invention adopts two scheduling and optimization schemes for the optimization strategy of the number of jammers: jammer number scheduling 1 and jammer number scheduling 2. Among them, the jammer number scheduling 1 is shown in the following table as a schematic code:

[0094]

[0095]

[0096] Further jammer number scheduling 2 is shown in the following table:

[0097]

[0098] In specific implementation, N i is the total number of algorithm iterations; N c is the jammer number scheduling index, the fitness of the main population does not change once, N c The larger the value, the stronger the algorithm's number search capability will be. However, the number of inferior solutions will also increase as the number of jammers increases. It is generally selected as one-fourth of the total number of iterations. d is the base probability of number reduction scheduling, p a The basic probability of increasing the number of scheduling is that after the jammer number scheduling 1 fails, the jammer number scheduling 2 can still maintain the number scheduling capability.

[0099] In summary, the improvement process of the master-slave parallel genetic algorithm is shown in the schematic code in the following table:

[0100]

[0101]

[0102] Finally, in S1500, the embodiment of the present invention achieves comprehensive interference suppression on the communication network through a preset multi-objective optimization function.

[0103] Specifically, the multi-objective optimization function is used to balance the interference signal power, the number of communication nodes and the number of jammers to balance the interference effectiveness and resource utilization efficiency. The multi-objective optimization function F is:

[0104]

[0105] The multi-objective optimization function includes a global interference effectiveness coefficient E, a system working time coefficient T, a spatial aggregation penalty coefficient S and a quantity penalty coefficient Q;

[0106] Among them, a1, a2, a3, and a4 are weighted coefficients, representing the importance of each indicator to the optimization of distributed communication interference deployment and power scheduling, and can be adjusted according to various anti-interference requirements;

[0107] The preset objective function F1 is used to focus on the deployment of a large number of low-power jammers; in the distributed communication jamming scenario, there is currently little research on the objective function that accurately describes the decentralized deployment of low-power communication jammers and the scheduling of jamming resources; the present invention quantifies and maximizes the global jamming effectiveness in the distributed communication jamming system through the objective function F1, which is particularly suitable for the deployment of low-power communication jammers;

[0108] The preset objective function 2 F2 is used to strengthen the restriction on the number of jammers and minimize the deployment of jamming resources;

[0109] The global interference effectiveness coefficient E is a weighted step interference-to-signal ratio that maximizes the global interference effectiveness in distributed communication interference;

[0110] The system working time coefficient T is used to measure the continuous working time of the jammer battery;

[0111] The spatial aggregation penalty coefficient S is used to ensure that the jammers are distributed as dispersed as possible;

[0112] The quantity penalty coefficient Q is used to balance the total number of jammers and the jamming power to meet different anti-jamming requirements.

[0113] The objective function and master-slave parallel genetic algorithm proposed in this paper have the characteristics of saving interference resources and searching for the optimal interference scheme quickly in simulating the construction of low-power distributed communication interference scenarios.

[0114] Considering the communication nodes in the distributed communication jammer equipment, one is that they can be pre-deployed in the battlefield area to form a spectrum environment that is favorable to the jammer; the other is that they can be thrown to key points by drones and other means during tactical operations to form regional short-term communication suppression interference. In the communication jammer scenario, the signal strength loss is small within a very close distance. For the convenience of calculation, the grid is divided into parallel lines with equal intervals of twenty meters, and each intersection is the optional layout position coordinate of the jammer. The present invention first forms 1300×1300 intersections, where the horizontal and vertical coordinates range from 0 to 1299.

[0115] In interference optimization, the present invention selects the maximum receiving power of the communication node as the target of interference suppression to achieve effective interference suppression of all communication receiving signals:

[0116]

[0117] Among them, P js , G rt 、h j are the received power, antenna gain and height of the jth communication node, P Tj′ , G tr 、h j′ , L a , L b are the transmission power, antenna gain, height, polarization loss, and filtering loss of the j′th communication node, r jj′ is the straight-line distance between nodes.

[0118] For the weighted step interference signal ratio coefficient, first, record G jr is the antenna gain of the jammer in the receiving direction of the communication node, G rj is the antenna gain of the communication node antenna in the direction of the jammer antenna; P Tj is the transmission power of the jth jammer, in W; h i is the jammer antenna height, r ij is the distance between the i-th jammer and the j-th communication node, in meters; L jb is the filtering loss of the interference signal. The interference power P of the i-th jammer entering the j-th communication node is obtained ij ,as follows:

[0119]

[0120] When electromagnetic waves propagate in free space, there is no natural or man-made environmental noise. The interference and communication signals are approximately in the same frequency band. The use of distributed jammers enables the interference signal to have a power superposition effect within the line-of-sight propagation range. To simplify the model, the present invention is based on the effective interference distance between a single jammer and a single communication node. Let x ij ∈{0,1} indicates whether the i-th jammer can interfere with the j-th communication node, that is, the statistically calculated interference distance is the distance at which the interference threshold reaches a certain value; then the interference-to-signal ratio when the j-th communication node is interfered is:

[0121]

[0122] In order to quantify the value of the interference-to-signal ratio when a single communication node is suppressed by interference and avoid excessive use of interference resources on individual communication stations, a step function is introduced to express it as:

[0123]

[0124] The purpose of distributed communication interference is to effectively suppress the entire wireless communication network and maximize the global interference efficiency. The weighted step interference signal ratio is:

[0125]

[0126] Where: E is the global interference effectiveness, A1 is the convergence scaling factor of the weighted objective function, M is the number of communication nodes, and w j is the threat level of the jth communication node.

[0127] For the system working time coefficient T, when the distributed communication jamming system is working, if the jammer battery energy is insufficient, the jamming signal power will be lower than the normal value. Therefore, the system working time is the time when the first jammer fails due to power failure, resulting in the weakening of the multi-source simultaneous jamming capability:

[0128]

[0129] Among them, A2 is the convergence scaling factor of the weighted objective function, η is the maximum usage limit of the battery power required for normal interference, and U out is the battery operating voltage, in V, E e is the jammer battery capacity, in AH, Y is the number of items that consume power for a jammer, P Tio is the power consumed by the oth item of the i-th jammer, in W, which is divided into the interference signal transmission power and the non-interference signal transmission power.

[0130] The spatial aggregation penalty coefficient S used to ensure that the jammers are distributed as dispersed as possible is that the distributed jammers should be deployed as dispersed as possible to avoid the jamming signals being concentrated and destroyed by the enemy's direction finding. That is, the spatial aggregation penalty coefficient is:

[0131]

[0132] Among them, A3 is the convergence scaling factor of the weighted objective function, x i ,y i is the horizontal and vertical coordinates of jammer i, is the horizontal and vertical coordinates of the jammer i2, r min is the penalty distance between jammers, in meters.

[0133] The number penalty coefficient Q used to balance the total number of jammers and the jamming power to meet different anti-jamming requirements is:

[0134]

[0135] Among them, A4 is the convergence scaling factor of the weighted objective function, M is the number of enemy UHF communication nodes, and N is the number of jammers used.

[0136] In the implementation of the present invention, the spatial aggregation penalty coefficient is used for the dispersed deployment of jammers to avoid the concentration of jamming signals and destruction by enemy direction finding. The horizontal and vertical coordinates of the jammers are set; the initial coordinates of the jammers are set according to the task requirements; and the jammers are ensured to be within the range of the dispersed deployment. And it is set as the convergence scaling coefficient of the weighted objective function; the distance between the jammers is calculated; and the distance penalty coefficient is set. It is set as the penalty distance between each jammer; the minimum penalty distance is set according to the jamming effect; and the jammers are ensured to be outside the set penalty distance range.

[0137] When the number penalty coefficient is used to balance the relationship between the total number of jammers and power, it is first set as the convergence scaling coefficient of the weighted objective function; then the penalty coefficient is set according to the jamming task; the objective function is adjusted to balance the number of jammers and power, that is, it is set to the number of enemy ultra-short wave communication nodes; the distribution and number of enemy communication nodes are evaluated; and the number of jammers is adjusted according to the evaluation results. According to the determined optimal number of jammers, ensure that the number of jammers meets the jamming task requirements.

[0138] Furthermore, in the distributed communication interference resource scheduling method of the present invention, before the distributed population is distinguished into a master population and a slave population, it also includes:

[0139] Analyze the distribution and number of communication nodes in a distributed communication network;

[0140] Determine the initial location and number of jammers;

[0141] Establish constraints on interference resources.

[0142] In specific implementation, in the distributed communication jamming scenario of the present invention, the following constraints are set according to the battlefield and equipment usage requirements:

[0143]

[0144] The meanings of each constraint are as follows:

[0145] Constraint ①: Each jammer must be outside the prohibited radius of each communication node to prevent the jammer from being discovered and destroyed due to being too close;

[0146] Constraint ②: The interference signal power sent is the rated value, which is in line with the actual situation of existing equipment;

[0147] Constraint ③: To ensure the networking coordination between jammers, the distance between any jammer and the nearest jammer must be within the maximum communication distance;

[0148] Constraint ④: All communication nodes must meet the minimum suppression ratio, that is, to achieve comprehensive interference suppression of the communication network.

[0149] The present invention sets constraints to ensure system stability and improve the interference effect, which is also from the perspective of reasonable resource allocation, balanced multi-objective optimization and adaptation to different scene requirements. Through the introduction of constraints, in the multi-objective optimization process, the relationship between different objectives can be balanced, such as interference suppression effect and resource utilization efficiency, so as to find the optimal solution. In addition, the constraints enable the optimization process to be dynamically adjusted to meet the needs of different interference scenarios, enhance the adaptability and flexibility of the solution, and further improve the efficiency of the algorithm.

[0150] Furthermore, when the present invention is implemented, a simulation verification implementation will also be provided to verify the innovation and uniqueness of the embodiments of the present invention. Specifically, in this simulation experiment, the processor used is Intel(R) Core(TM) i5-8300H CPU@2.30GHz, the machine memory is 16.0GB, the graphics card is NVIDIA GTX1080Ti, and the conda environment is installed.

[0151] A confrontation zone of 26km in length and 26km in width is set. The threat level of the communication node, the forbidden radius of the jammer deployment, the node coordinates, and the known fixed quantities are randomly generated. The maximum receiving power of each communication device is the maximum value of the receiving power between all devices. The following parameters are used in the simulation scenario: the jammer antenna gain is 1.5dB, the communication antenna gain is 4dB, the jammer and the communication antenna height are fixed; the polarization loss of the interference and communication signals is 1, the filtering loss is 0.8, and the interference signal to communication signal interference ratio threshold is 3; the jammer battery capacity is 24AH, the output voltage is 24V, the non-interference signal transmission power is 4w all the time, and the effective working threshold of the power is 0.95; the scaling factors are 1, 0.5, 3, and 12 respectively. After selecting the normal communication node receiving signal power, in the scenario of one jammer to one communication node, the interference signal transmission power and the longest calculated statistical interference distance are set as shown in the following table, and used in the simulation experiment of the present invention.

[0152] Interference power w 2 4 8 12 16 20 Available distance m 5621 6685 7950 8798 9454 9996

[0153] The generation of distributed communication interference schemes requires finding an effective interference scheme that meets the requirements of building a communication interference environment in a short time, that is, a smaller convergence cycle and a faster convergence speed are required. Therefore, the parameter settings of the improved particle swarm algorithm in the improved master-slave parallel genetic algorithm are shown in the following table.

[0154] parameter T NP M N N pd pa Numeric 100 10 12 12 35 0 0.05

[0155] Where: T is the maximum number of iterations; NP is the number of chromosome sets; M is the maximum number of available jammers; N is the number of ultra-short wave radio stations.

[0156] In order to enhance the pertinence of the simulation experiment, the present invention simulates two types of communication node deployment methods, such as communication node deployment one in FIG3 (a), which simulates a communication network system laid out along the traffic road, and communication node deployment two in FIG3 (b), which simulates a communication network system that is overall centralized and scattered.

[0157] Master-slave parallel genetic algorithm simulation verification and result analysis:

[0158] The master-slave parallel genetic algorithm proposed in the present invention is simulated and compared with the adaptive grid particle swarm algorithm, the genetic particle swarm algorithm, the genetic algorithm, and the simulated annealing algorithm. The simulated annealing algorithm generates two new solutions in one iteration, and iterates 500 times in total. The algorithm of the present invention has strong adaptability and can change the algorithm links, objective functions, and the weights of objective functions to achieve different working modes. The weight settings of objective functions in different working modes are shown in the following table.

[0159]

[0160] Further, the experiment of the fixed number of jammers is set to simulate the situation of the scarcity of the number of jammers and to test the ability of the algorithm to complete the construction of the communication interference environment with the minimum number of jammers. According to previous experiments, the two communication node deployments when the number of jammers is three or four are feasible solutions that meet the constraints. When the master-slave parallel genetic algorithm searches for the interference scheme with the minimum number of jammers, the objective function 2 of the present invention is used to cancel the scheduling strategy of the number of jammer poles and the fixed position power scheduling strategy of the optimal solution for the first and midway, so as to enhance the algorithm's search ability for feasible solutions and speed up the iteration speed. The experimental results of the minimum number of scenarios are shown in the following table, which are the statistical values ​​of each algorithm running independently 30 times. It can be seen that the search ability of the minimum number of jammers of the above algorithms is greatly affected by the layout of communication nodes, and the degree of improvement is different, but the master-slave parallel genetic algorithm has advantages in the number of successes, the average power of the jammer, the average maximum interference power, and the effective time.

[0161]

[0162]

[0163] Furthermore, Experiment 2 is a high-speed confrontation scenario. The construction of a large-scale communication interference environment will increase the demand for the number of jammers, the computational complexity will be significantly improved, and the compatibility of the high-speed running algorithm with other devices will also be enhanced. In Experiment 2, in order to reduce the impact of no feasible solution on statistics, the communication node deployment 2 is adopted. When the number of jammers with a solution rate of each algorithm greater than 90%, it is run 15 times in sequence, and the average of the first ten solution data is taken as a comparison. Because the number of jammers is fixed, the number scheduling strategy of the master-slave parallel genetic algorithm is cancelled. The comparison results under the high-speed confrontation scenario are shown in the following table. It can be seen that the algorithm of the present invention has advantages in algorithm running time, average jammer power, average maximum interference power, and effective time.

[0164]

[0165] Furthermore, the present invention also provides Experiment 3 to simulate the actual communication interference scheme generation process. Because the compared algorithms lack optimization for the number of jammers, it is often necessary to manually traverse a certain number of jammers and select the interference scheme with the highest fitness value as the number of jammers, which is relatively inefficient. This algorithm innovatively proposes a jammer number scheduling strategy to enhance the algorithm search speed and reduce the amount of manual operation. The simulation of Experiment 3 is divided into two groups:

[0166] When the communication node is deployed, the five algorithms are used to traverse the number of jammers from one to twelve. The number of jammers corresponding to the algorithm with the highest fitness value is searched three times again. The one with the highest fitness value is selected as the final jammer scheme among the twelve jammer schemes, and three search modes of the jammer are tested. Mode 1 is a balanced mode with balanced weights; Mode 2 is a mode that emphasizes the duration of interference under the control of the number of jammers; Mode 3 is a mode that emphasizes the effect of interference under the control of the number of jammers. As shown in the following table, the algorithm mode 1 of the present invention can use more low-power jammers to set up a distributed communication interference environment to improve the fitness of the objective function when the number of jammers is weakly penalized, and extend the effective working time of the system as much as possible; the algorithm modes 2 and 3 of the present invention respectively optimize the reduction of interference power to extend the effective working time and focus on the interference effect under the premise of controlling the number of jammers, and the algorithm running time is much shorter than that of mode 1; the algorithm of the present invention has a stronger optimization ability than the comparison algorithm under the traversal and repeated search strategy.

[0167]

[0168] Furthermore, by providing experimental group 2, the algorithm of the present invention uses a jammer number scheduling strategy. In the communication node deployment one, after selecting the initial number of jammers, each independently runs 10 times and takes the average. The results of the number scheduling scenario comparison 2 are shown in the following table. The running time, power, and duration of the algorithm of the present invention are stronger than the experimental results of experimental group one. The number of jammers in mode one is affected by the initial number of jammers, and modes two and three can effectively control the number of jammers; when traversed only once, the fitness of each mode under each initial number of jammers is roughly equivalent to that of experimental group one, and some have surpassed it. After adding the number scheduling strategy, within the same time, the efficiency of the algorithm of the present invention in searching for the optimal interference solution will be higher than the search method of experimental group one, proving the effectiveness of the number scheduling strategy.

[0169]

[0170]

[0171] Furthermore, the present invention also provides a comparative experiment on the objective function. For example, when the communication node is deployed one, the mode one of the objective function one and the mode three of the objective function two of the present invention are selected, and after combining the background and restriction conditions of the present invention, as shown in formulas (1), (2), and (3).

[0172]

[0173] With a fixed initial number of jammers, the master-slave parallel genetic algorithm of the present invention is used to run 10 times independently under different objective functions, and the average values ​​are shown in Table 8.

[0174] In the distributed communication interference scenario, there is currently little research on the objective function that accurately describes the decentralized deployment of low-power communication jammers and the scheduling of interference resources. The comparison objective function one is the interference scenario of multiple jammers on networked radars, the comparison objective function two is the scenario of aerial jammers interfering with the ground, and the comparison objective function three is the interference scenario of multiple jammers on multiple communication nodes. As can be seen from the table below, the objective functions of other interference countermeasure scenarios, the number search and power scheduling capabilities have a significant downward trend as the initial number of jammers increases, that is, the number of jammers and the power scheduling capabilities are relatively weak. In addition to controlling the interference power, the objective function one of the present invention can use more jammers, and the objective function two of the present invention is less affected by the initial number of jammers, and the ability to construct a communication interference environment using the minimum number of jammers and the lowest interference power is stable.

[0175]

[0176]

[0177] The above detailed simulation experiments show that compared with the adaptive grid particle swarm algorithm, genetic particle swarm algorithm, genetic algorithm, and simulated annealing algorithm, the proposed algorithm is stable and reliable in seeking the minimum number of jammers and high-speed confrontation. Under the best solution search, the optimal jamming solution can be quickly obtained without relying on the initial jammer number setting. Compared with the objective functions of multiple jammers for networking radars, air jamming to cover ground nodes, and multiple jammers for multiple communication nodes, the objective function of the present invention can save interference power, and one side of the objective function focuses on the deployment of a large number of low-power jammers, and the second objective function is stably deployed with the least interference resources.

[0178] In summary, the present invention proposes a new distributed communication interference resource scheduling method. It adapts to the scheduling needs of various environments. By establishing a distributed communication interference scenario and a resource allocation model, two objective functions are proposed. Combined with the master-slave parallel genetic algorithm, the adaptive scheduling needs of various environments are realized. The comprehensive benefit is maximized. The objective function comprehensively considers the interference suppression effect, the system working time, the number of jammers and the distance between communication nodes. Through the weight method, the interference scheme search with the maximum comprehensive benefit can be realized. Through the improved genetic algorithm, the number of jammers and power scheduling strategies are proposed for the defects that the standard genetic algorithm cannot efficiently schedule the number of jammers, the interference power scheduling efficiency is low, and the convergence speed is slow. The master-slave parallel genetic algorithm is improved to improve the convergence performance and efficiency of the algorithm. Through the optimization model and the improved algorithm, the interference resources are saved, and in the simulation of the low-power distributed communication interference scenario, the optimal interference scheme can be quickly searched to ensure the efficient use of interference resources. In addition, this scheme weights the interference signal power, the number of communication nodes and the number of jammers through a multi-objective optimization model to achieve comprehensive interference suppression of the communication network system and ensure the maximization of system efficiency. Furthermore, it is possible to dynamically adjust and iterate the optimization. During the optimization process, the value of the objective function is calculated in real time, and the weight of the objective function is dynamically adjusted to adapt to the needs of different interference scenarios. Through continuous iteration, the global optimal interference solution is searched. The present invention is adaptable to a variety of scenarios, ensuring that the system can achieve the best interference effect in a variety of scenarios, and has strong robustness and adaptability. It can be seen that the distributed communication interference resource scheduling method not only improves the interference effectiveness and resource utilization efficiency, but also has high flexibility and adaptability, and can operate efficiently in a variety of complex environments.

[0179] Please refer to Figure 2 , Figure 2 Schematic diagram of the basic structure of the distributed communication interference resource scheduling device of this embodiment.

[0180] like Figure 2 As shown, a distributed communication interference resource scheduling device includes: a population division module 1100, a population initialization module 1200, an interference initialization module 1300, a dynamic adjustment module 1400 and an interference suppression module 1500. Among them,

[0181] A population division module 1100 is used to divide the distributed population into a master population and a slave population, wherein the master population is responsible for updating the global optimal solution and the slave population is responsible for independent evolution;

[0182] A population initialization module 1200 is used to complete the initialization in a roulette manner, where each slave population runs independently to perform local mutation and full replication crossover operations;

[0183] The interference initialization module 1300 is used to adjust the interference power to the maximum level during communication interference initialization, select the sub-population interference assignments that meet the preset constraints, and calculate the sub-population fitness;

[0184] A dynamic adjustment module 1400 dynamically adjusts power scheduling and the number of jammers according to the fitness values ​​of the master population and the slave population;

[0185] The interference suppression module 1500 is used to achieve comprehensive interference suppression on the communication network through a preset multi-objective optimization function.

[0186] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0187] The above detailed description of the embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0188] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0189] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with those in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0190] It will be understood by those skilled in the art that the term "terminal" as used herein includes both devices with wireless signal receivers, which are devices with only wireless signal receivers without transmission capabilities, and devices with receiving and transmitting hardware, which are devices with receiving and transmitting hardware capable of performing two-way communications over a two-way communication link. Such devices may include: cellular or other communication devices, which have a single-line display or a multi-line display or a cellular or other communication device without a multi-line display; PCS (Personal Communications Service), which may combine voice, data processing, fax and / or data communication capabilities; PDA (Personal Digital Assistant), which may include a radio frequency receiver, a pager, Internet / intranet access, a web browser, a notepad, a calendar and / or a GPS (Global Positioning System) receiver; conventional laptop and / or palmtop computers or other devices, which have and / or include a conventional laptop and / or palmtop computer or other device with and / or including a radio frequency receiver. The "terminal" used here can be portable, transportable, installed in a vehicle (air, sea and / or land), or suitable for and / or configured to run locally, and / or in a distributed form, running at any other location on the earth and / or in space. The "terminal" used here can also be a communication terminal, an Internet terminal, a music / video playing terminal, for example, a PDA, a MID (Mobile Internet Device) and / or a mobile phone with a music / video playing function, or a smart TV, a set-top box and other devices.

[0191] The hardware referred to by the names such as "server", "client", and "service node" in this application is essentially an electronic device with the equivalent capabilities of a personal computer. It is a hardware device with the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. The computer program is stored in its memory, and the central processing unit calls the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0192] It should be pointed out that the concept of "server" referred to in this application can also be extended to the case of server clusters. According to the network deployment principle understood by those skilled in the art, the servers should be logically divided. In physical space, these servers can be independent of each other but can be called through interfaces, or integrated into a physical computer or a set of computer clusters. Those skilled in the art should understand this flexibility, and should not use it to restrict the implementation of the network deployment method of this application.

[0193] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for access.

[0194] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as it is suitable for being called by the technical solution of this application.

[0195] Those skilled in the art should be aware that, although the various methods of the present application are described based on the same concept and thus present commonality to each other, unless otherwise specified, these methods can be independently executed. Similarly, for each embodiment disclosed in the present application, they are all proposed based on the same inventive concept, therefore, concepts with the same expression, and concepts that are appropriately changed for convenience despite different expressions, should be understood as equivalent.

[0196] Unless the mutually exclusive relationship between the embodiments to be disclosed in this application is explicitly stated, the relevant technical features involved in each embodiment can be cross-combined to flexibly construct a new embodiment, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or solve certain deficiencies in the prior art. Those skilled in the art should be aware of this flexibility.

[0197] Those skilled in the art will appreciate that the various operations, methods, steps, measures, and schemes in the processes discussed in this application may be alternated, altered, combined, or deleted. Further, other steps, measures, and schemes in the various operations, methods, and processes discussed in this application may also be alternated, altered, rearranged, decomposed, combined, or deleted. Further, the steps, measures, and schemes in the prior art that are similar to those disclosed in this application may also be alternated, altered, rearranged, decomposed, combined, or deleted.

[0198] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A distributed communication interference resource scheduling method, characterized in that: include: The distributed population is divided into a master population and a slave population, wherein the master population is responsible for updating the global optimal solution and the slave population is responsible for independent evolution; Initialization is completed in a roulette manner, and each of the slave populations runs independently to perform local mutation and full replication crossover operations; When the communication interference is initialized, the interference power is adjusted to the maximum level, the sub-population interference deployment that meets the preset constraints is selected, and the sub-population fitness is calculated; Dynamically adjust power scheduling and the number of jammers through the fitness values ​​of the master population and the slave population; Comprehensive interference suppression of the communication network is achieved through a preset multi-objective optimization function.

2. The distributed communication interference resource scheduling method according to claim 1, characterized in that: The distributed population is divided into a master population and a slave population, and the initialization is completed in a roulette manner. Each slave population runs independently and performs a local mutation and a full replication crossover operation, including: The diversity of individuals in the population is determined by using a local mutation method to perform mutation operations on individuals in the population and a full replication crossover method. The master population is generated by cross-pollination of the historically optimal slave populations to guide the power scheduling of the slave populations; After the master population is updated, it will be immediately crossed to all slave populations, and each of the slave populations will perform mutation and crossover operations independently.

3. The distributed communication interference resource scheduling method according to claim 1, characterized in that: The dynamically adjusting power scheduling and the number of jammers by using the fitness values ​​of the master population and the slave population includes: Determining a power scheduling strategy according to the fitness values ​​of the master population and the slave population; According to the fitness value of the slave population, the probability of power increase or power decrease is adjusted to optimize the power scheduling strategy; After updating the fitness value, the master population transmits the new power scheduling strategy to each slave population through the communication mechanism: Dynamically monitor the execution of the power scheduling strategy and dynamically adjust the interference signal power and interference location based on real-time data to ensure effective coverage of the interference signal and maximize the interference effectiveness.

4. The distributed communication interference resource scheduling method according to claim 1, characterized in that: The method of dynamically adjusting power scheduling and the number of jammers by using the fitness values ​​of the master population and the slave population further includes: By generating random numbers, the number and location of jammers can be dynamically adjusted; According to a preset interference environment, a basic probability value for scheduling the number of jammers is set to control the increase and decrease of the number of jammers; When the fitness value of the main population is not updated, triggering the jammer number scheduling operation; Through multiple jammer number scheduling, the distribution of the number of jammers is determined to maximize the jamming effectiveness.

5. The distributed communication interference resource scheduling method according to claim 1, characterized in that: The multi-objective optimization function is used to balance the interference signal power, the number of communication nodes and the number of jammers to balance the interference effectiveness and resource utilization efficiency. The multi-objective optimization function F is: The multi-objective optimization function includes a global interference effectiveness coefficient E, a system working time coefficient T, a spatial aggregation penalty coefficient S and a quantity penalty coefficient Q; Among them, a1, a2, a3, and a4 are weighted coefficients, representing the importance of each indicator to the optimization of distributed communication interference deployment and power scheduling, and can be adjusted according to various anti-interference requirements; The preset objective function F1 is used to focus on the deployment of a large number of low-power jammers; The preset objective function 2 F2 is used to strengthen the restriction on the number of jammers and minimize the deployment of jamming resources; The global interference effectiveness coefficient E is a weighted step interference-to-signal ratio that maximizes the global interference effectiveness in distributed communication interference; The system working time coefficient T is used to measure the continuous working time of the jammer battery; The spatial aggregation penalty coefficient S is used to ensure that the jammers are distributed as dispersed as possible; The quantity penalty coefficient Q is used to balance the total number of jammers and the jamming power to meet different anti-jamming requirements.

6. The distributed communication interference resource scheduling method according to claim 1, characterized in that: The method of achieving comprehensive interference suppression on the communication network through a preset multi-objective optimization function includes: According to different anti-interference requirements, dynamically adjust the weighted weights of the multi-objective optimization function for the interference signal power, the number of communication nodes and the number of jammers; wherein the weighted convergence of the objective function is adjusted by a scaling factor; The value of the multi-objective optimization function is calculated in real time, and the value corresponding to the global optimal interference is searched through continuous iteration.

7. The distributed communication interference resource scheduling method according to claim 1, characterized in that: Before the distributed population is divided into a master population and a slave population, the method further includes: Analyze the distribution and number of communication nodes in a distributed communication network; Determine the initial location and number of jammers; Establish constraints on interference resources.

8. The distributed communication interference resource scheduling method according to claim 1, characterized in that: The initialization is completed in a roulette manner, and further includes: By setting the algorithm parameters, including the maximum number of iterations, the number of chromosome sets, the maximum number of available jammers, and the number of UHF radio stations; Generate initial chromosomes in the main population, each chromosome represents a possible interference scheme; Generate auxiliary chromosomes from the population for local search and fine-tuning; The interference effect of the newly generated population individuals is evaluated according to the preset fitness function, the population individuals with higher fitness are retained in the main population, and the population individuals with lower fitness are eliminated.

9. The distributed communication interference resource scheduling method according to claim 5, characterized in that: The spatial aggregation penalty coefficient S is specifically: Among them, A3 is the convergence scaling factor of the weighted objective function, x i ,y i is the horizontal and vertical coordinates of jammer i, is the horizontal and vertical coordinates of the jammer i2, r min is the penalty distance between jammers, in meters.

10. The distributed communication interference resource scheduling method according to claim 5, characterized in that: The quantity penalty coefficient Q is specifically: Among them, A4 is the convergence scaling factor of the weighted objective function, M is the number of enemy UHF communication nodes, and N is the number of jammers used.

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