Adaptive Method Of Communication Waveform Of Aerospace TT&C Network Based On Deep Learning Model

With the development of measurement and control communication technology,the demand for communication quality and capacity is increasing day by day.However,in the transmission process of measurement and control communication,the complex channel environment,limited transmission resources and other factors limit the communication performance of measurement and control communication system.In the case of extremely limited transmission resources,an efficient and high quality transmission mode is one of the research hotspots of measurement and control communication transmission.In order to solve this problem,this paper focuses on the adaptive modulation technology and adaptive power distribution technology of waveform adaptive technology.In this thesis,the modeling of Rayleigh fading channel is used to study the problem of adaptive transmission based on modulation in time-varying channels.Based on the traditional adaptive modulation system,the Reinforcement Learning and Neural Network(Reinforcement Learning and Neural Network,RLNN)algorithm is introduced to build a communication adaptive modulation system based on Deep Reinforcement Learning.Through the Deep Reinforcement Learning algorithm,the channel changes are learned.The behavior strategy is selected to improve the BER and spectral efficiency of the system by selecting the best modulation mode suitable for the current channel.To solve the problem of low exploration efficiency of original Deep Reinforcement Learning,an improved Deep Reinforcement Learning algorithm is proposed,which improves the fixed exploration strategy of SNR selective modulation to the adaptive exploration strategy,and improves the utilization efficiency of available spectrum in Rayleigh fading channel.The simulation results show that the bit error rate and spectral efficiency of the improved adaptive modulation system are better than that of the original deep reinforcement learning modulation system,and the communication performance of the system can be improved effectively.In addition,a deep double Q network algorithm is proposed to solve the adaptive power allocation problem of orthogonal frequency division multiplexing system.The complex dynamic power allocation algorithm makes the agent dynamically allocate power,and in order to balance the system capacity and user fairness,to join the fairness constraints in the objective function,can adjust the fairness constraint value according to the actual demand,through learning the experience of past,finally get close to the optimal solution of distribution strategy.The simulation results show that the proposed algorithm can achieve higher system capacity than the traditional adaptive power allocation algorithm.