Reducing Interferences in Wireless Communication Systems by Mobile Agents with Recurrent Neural Networks-based Adaptive Channel Equalization

Solving channel equalization problem in communication systems is based on adaptive filtering algorithms. Today, Mobile Agents (MAs) with Recurrent Neural Networks (RNNs) can be also adopted for effective interference reduction in modern wireless communication systems (WCSs). In this paper MAs with RNNs are proposed as novel computing algorithms for reducing interferences in WCSs performing an adaptive channel equalization. The method to provide it is so called MAs-RNNs. We perform the implementation of this new paradigm for interferences reduction. Simulations results and evaluations demonstrates the effectiveness of this approach and as better transmission performance in wireless communication network can be achieved by using the MAs-RNNs based adaptive filtering algorithm.