Design Of Anti-Jamming Communication System Based On Intelligent Decision

With the continuous development of wireless communication technology,the electromagnetic environment is becoming more and more complicated.The traditional anti-jamming method lacks flexibility and cannot dynamically adapt to scene changes.The study of efficient and reliable intelligent anti-jamming solutions is imperative.In this paper,an anti-jamming communication system based on intelligent decision-making is designed.Deep learning and reinforcement learning algorithms are applied to intelligent decision-making.Through the analysis and learning of the transmission environment,the intelligent selection of channel and communication parameters is completed to improve the anti-jamming ability of the system.First,the traditional anti-jamming technology is summarized,and the adaptive anti-jamming technology is analyzed and researched.It is pointed out that the above scheme has the problems of low work efficiency,lack of flexibility and poor self-learning ability.Based on this,the intelligent anti-jamming communication system architecture is proposed,which lays the foundation for the subsequent chapter research.Next,the channel decision algorithm in the intelligent anti-jamming communication system is studied.A decision algorithm based on SARSA and Q learning is proposed.When the interference type and the wireless channel model are unknown,the channel decision can be completed by interacting with the channel.Aiming at the problem of slow convergence speed of fixed mode interference,an improved Q learning algorithm is proposed.Simulation results show that the proposed algorithm can greatly improve the convergence speed.Then,the intelligent parameter decision algorithm is studied,and the decision engine is divided into an optimization decision module and an inference decision module.The objective function is designed,and intelligently selects communication parameters such as modulation method,coding method,power and length of spread spectrum code according to the criteria of minimizing bit error rate,maximizing transmission rate and minimum transmission power.In the optimization decision module,a decision system based on genetic algorithm,binary particle swarm algorithm and artificial bee colony algorithm is designed;in the inference decision module,Q learning algorithm is used to obtain the optimal parameters through interaction with the environment.In order to solve the problem that the above algorithm can not cope with questions of larger state scale,a reasoning decision algorithm based on DQN and its improvement is proposed.The simulation results show the superior performance of the proposed algorithm.Finally,an anti-jamming communication system architecture based on intelligent decision-making is designed,and detailed system implementation steps are given.Different processing schemes can be adaptively selected for interference-free and interference-free situations.System simulation shows the effectiveness and feasibility of the proposed scheme.