Intelligence-based Channel Equalization for 4×1 SFBC-OFDM Receiver

摘要

This research paper represents an intelligent receiver based on the artificial-neural-networks (ANNs) for a 4x1 space-frequency-block-coded orthogonal-frequency-division-multiplexing (SFBC-OFDM) system, working under slow time-varying frequency-selective fading channels. The proposed equalizer directly recovers transmitted symbols from the received signal, without the explicit requirement of the channel estimation. The ANN based equalizer is modelled by using feedforward as well as the recurrent neural-network (NN) architectures, and is trained using error backpropagation algorithms. The major focus is on efficiency and efficacy of three different strategies, namely the gradient-descent with momentum (GDM), resilient-propagation (RProp), and Levenberg-Marquardt (LM) algorithms. The recurrent neural network architecture based SFBC-OFDM system is found to be an appropriate choice in terms of the low bit-error-rate performance, while using different quasi-orthogonal space-time block codes.