Topology Inference And Jamming Waveform Design For Wireless Competitive MAC Networks

Obtaining the network topology of the opponent is one of the research focuses of cognitive electronic countermeasure.The key problem is how to use the available network information to identify the network topology from the perspective of actual combat scenarios,and to speed up the selection of jamming waveforms through the analysis of the topology.In order to solve the above problems,some algorithms which analyze the communication signal of the target network in electronic reconnaissance,and analyze the key nodes according to the network topology structure are implemented in this thesis.Based on the analysis results,some nodes in the network are selected to use appropriate jamming waveforms to attack,in order to speed up the selection of jamming waveforms and improve the jamming effect.Specifically speaking,the work in this thesis is as follows:Aiming at the characteristics of wireless network based on competition access modes,a density-based clustering method is used to cluster and match the sampling signals in order to acquire the pairs of nodes with connectivity relationship in the network in this thesis,and to identify the topology of the network.In this thesis,network simulation experiments are carried out with different network sizes,different network topologies and different access protocols.The results of experiments show that the algorithm can identify network topology in different situations.After identifying the topology of the network,in order to speed up the selection of jamming waveforms and improve the jamming effect,the topology structure of the network is analyzed in this thesis.Network nodes are grouped based on spectral clustering and based on modular degree,respectively,with or without prior information.The network topology is divided into groups to obtain some key nodes in the network.The simulation results show that the selected network node will cause more damage to the network after attacked.The node selected based on network analysis can be used as the target for attacked,and the network analysis results can be applied to the jamming waveform design.Finally,in the design of jamming waveforms based on reinforcement learning,the rate of packet change in the network is used as the reward value in the process of reinforcement learning.The jamming waveforms for the target network are successfully selected through the experiment,and the results based on the network topology inference are used as a priori knowledge,which effectively reduces the number of random explorations in the reinforcement learning,improves the convergence speed of the learning process,and obtains a larger cumulative reward value.As a result,better jamming effect is achieved.