Recurrent Fuzzy-Neural MIMO Channel Modeling

Abstract

Fuzzy systems and artificial neural networks (ANN), as important components of soft-computation, can be applied together to model uncertainty. A composite block of the fuzzy system and the ANN shares a mutually beneficial association resulting in enhanced performance with smaller networks. It makes them suitable for application with time-varying multi-input multi-output (MIMO) channel modeling enabling such a system to track minute variations in propagation conditions. Here we propose a fuzzy neural system (FNS) using a fuzzy time delay fully recurrent neural network (FTDFRNN) that has the capability to tackle time-varying inputs in fuzzified form and is used to model MIMO channels. The inference engine is constituted by novel FTDFRNN blocks which determine the decision boundaries and tracks in-phase and quadrature components of input signals encompassing stochastic behavior of the MIMO channel. The system shows significant improvement in performance compared to statistical and ANN approaches in terms of faster processing time, lower bit error rate (BER) margins and better precision while carrying out symbol recovery of transmitted data through severely faded MIMO channels.