Research On Communication Network C Ognition And Interference Strategy

With the rapid development of communication technology,the form of communication countermeasure is changing constantly.The two sides in the battlefield no longer only focus on the electronic attack and defense of individual equipment,but have shifted their focus to multinode network confrontation with a certain scale.This makes it difficult for traditional communication countermeasures to adapt to the current battlefield environment.Therefore,there is an urgent need to carry out relevant research on communication network countermeasure.Communication network cognition and interference belong to the frontier research content in communication network countermeasures,and there is still a lack of relevant research results.From the perspective of battlefield reconnaissance,this article starts with the physical layer signal,and finally develops a targeted interference strategy through the processes of communication individual identification,link identification,topology inference,and key node identification.The specific content is as follows:(1)Identifying communication nodes based on physical layer signals.The main work is to apply the multifractal theory and density clustering algorithm to signal feature extraction and individual identification of communication nodes,put forward a complete communication node identification process framework.Two problems,feature vector dimension reduction and clustering parameter setting,of density clustering algorithm were deeply explored,and the corresponding optimization scheme was put forward.The simulation results show that the proposed algorithm can achieve a high recognition rate under the experimental conditions.(2)Identifying communication links and inferring network topology based on node information.The main work is to verify the method of identifying communication link based on signal packet connection relation according to the node record under network simulation.Then,combined with the compressed sensing theory,the state of sending and receiving data by nodes is modeled,and the sparse vector reconstruction algorithm is used to solve the network connection matrix.In addition,the connection matrix reconstruction algorithm based on random sampling was proposed,which further confirmed the connection relationship by cumulative sampling results.The performance of the algorithm under different parameter Settings was verified by using NS3 simulation network.The results show that the algorithm has a high recognition rate for network topology under appropriate parameters.(3)Identifying key nodes and formulating interference strategies based on node information and network topology.The main work is to analyze the characteristics of different types of node features as key indicators,and put forward a key indicators fusion algorithm based on evidence theory,which converts various key indicators into evidence measures,and uses grouping synthesis and weighted average methods obtain comprehensive key indicators;In addition,an interference cost model is introduced,the interference decision is abstracted into a multiobjective combinatorial optimization problem,which can solved through a multi-objective evolutionary algorithm to obtain a targeted interference strategy.The simulation results show that the interference strategy obtained by the algorithm can effectively reduce the network efficiency and throughput of the target communication network.