Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

Abstract

In recent years, cooperative communication (CC) technology has emerged as a hotspot for testing wireless communication networks (WCNs), and it will play an important role in the spectrum utilization of future wireless communication systems. Instead of running node transmissions at full capacity, this design will distribute available paths across multiple relay nodes to increase the overall throughput. The modeling WCNs coordination processes, as a recurrent mechanism and recommending a deep learning-based transfer choice, propose a recurrent neural network (RNN) process-based relay selection in this research article. This network is trained according to the joint receiver and transmitter outage likelihood and shared knowledge, and without the use of a model or prior data, the best relay is picked from a set of relay nodes. In this study, we make use of the RNN to do superdimensional (high-layered) processing and increase the rate of learning and also have a neural network (NN) selection testing to study the communication device, find out whether or not it can be used, find out how much the system is capable of, and look at how much energy the network needs. In these simulations, it has been shown that the RNN scheme is more effective on these targets and allows the design to keep converged over a longer period of time. We will compare the accuracy and efficiency of our RNN processed-based relay selection methods with long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BLSTM),which are all acronyms for long short-term memory methods.