Wireless Channel State Prediction Method Based on Improved Adaptive and Parameter-Free Recurrent Neural Structure

Aiming at the problem of large error of channel state prediction caused by channel time-varying characteristics in wireless communication system, propose a wireless channel state prediction method based on improved adaptive and parameter-free recurrent neural structure(APF-RNS). The method though Aquila Optimizer for find the number of hidden layer units and the optimal value of learning rate of the neural network, using the optimal parameters to construct adaptive without recursive neural network. In this way, the convergence speed and fitting effect of the objective function of the neural network can be improved, and the problems of large prediction error and poor generalization ability of the neural network in the prediction process can be avoided, thereby improving the prediction accuracy of the channel state information. The simulation results show that compared with Genetic Algorithm, Particle Swarm Optimization and Sparrow Search Algorithm, the improved APF-RNS has better performance in the optimization ability and convergence speed. Meanwhile, it also has a significant improvement in prediction accuracy compared to the APF-RNS.