Performance of Neural Computing Techniques in Communication Networks

Abstract

This research investigates the use of neural computing techniques in communication networks and evaluates their performance based on error rate, delay, and throughput. The results indicate that different neural computing techniques, such as Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) and Generative Adversarial Networks (GANs) have different trade-offs in terms of their effectiveness in improving performance. The selection of technique will base on the particular requirements of the application. The research also evaluates the relative performance of different communication network architectures and identified the trade-offs and limitations associated with the application of different techniques in communication networks. The research suggests that further research is needed to explore the use of techniques, such as deep reinforcement learning; in communication networks and to investigate how the employment of techniques can be used to improve the security and robustness of communication networks.