Research On Adaptive Communication Jamming Technology Based On Physical Layer Parameter Decision-Making

The complex and ever-changing battlefield electromagnetic environment and the flourishing communication anti-jamming technology have set layers of obstacles to the jamming technology,making it difficult to achieve the desired jamming effectiveness.Control over the electromagnetic spectrum is the crucial factor in determining the victory or defeat of the battlefield in modern warfare.How to innovate the jamming technology to utilize the behavior changes and corresponding characteristics of communication signals to complete the jamming scheme is an urgent problem to solve in the current communication jamming field.Therefore,establishing an adaptive communication jamming system that can automatically adjust in the face of dynamically changing environments is an essential development direction for communication jamming technology.This paper investigates communication jamming technology based on physical layer parameter decision-making in conjunction with adaptive technologies,the details of which are as follows:Firstly,this paper proposes a cognitive jamming channel selection algorithm based on reinforcement learning,which completes the decision-making of channel parameters in the physical layer parameters of the jamming signal through the interaction with the environment information when the communication channel switching strategy is unknown.To address the problem that the channel tracking accuracy is not desirable under constant exploration probability,an improved Q-learning jamming channel selection algorithm based on the segmented adjustable greedy strategy is proposed to dynamically adjust the exploration probability,which improves the channel tracking accuracy.To address the problem that the state space and action space of the algorithm model increase dramatically and the convergence performance is unsatisfactory when channel diversity is used in communication systems,an improved DQN jamming channel selection algorithm based on the segmented adjustable greedy strategy is proposed,which can adapt to the large decision space and improve the channel tracking accuracy.To address the problem that jamming resources may be insufficient in wartime environments and the jamming power utilization needs to be maximized,the upper confidence bound is introduced to improve the DQN algorithm.The improved algorithm can prioritize jamming for high-threat communication users to improve the jamming power utilization.The improved algorithm has a faster convergence speed and higher channel tracking accuracy.Secondly,an adaptive jamming waveform pattern design algorithm based on the meta-heuristic algorithm is proposed in this paper,which uses the meta-heuristic algorithm to perform adaptive optimization design of jamming waveform patterns and complete the decision of waveform pattern parameters in the physical layer parameters of jamming signals.To address the problem that the current optimal jamming waveform analysis model does not consider the non-ideal reception situation,the optimal jamming waveform in the non-ideal reception state is derived and analyzed,and then the analysis results are simulated and verified,which extends the applicability of the optimal jamming waveform analysis model.To address the problem that the current jamming waveform decision algorithms can only select typical jamming waveforms,which are not optimal in non-ideal reception states,an improved adaptive jamming waveform design algorithm based on grey wolf optimization is proposed.In the ideal reception state,the improved algorithm has the same jamming effectiveness as the existing algorithms.In the nonideal reception state,the jamming waveform generated by the improved algorithm is no longer a typical jamming waveform,and the jamming performance is higher compared with the existing algorithms.In this paper,an adaptive communication jamming method that makes decisions on the physical layer parameters of the jamming signal is studied by using adaptive technologies.The method has important application value as it enables the jammer to autonomously generate the optimal jamming scheme according to the environmental state,and is equipped with accurate and rapid real-time adaptive jamming capability.