Neural Network Based MIMO-OFDM Channel Equalizer Using Comb-Type Pilot Arrangement

Abstract

MIMO (Multiple Input Multiple Output) and OFDM (Orthogonal frequency division multiplexing) bringing along a number of pros; a combination of both stands a good possibility of being the next-generation (4th generation) of mobile wireless systems. The technology however imposes a challenge that is the increased complexity of channel equalization. Wireless channels are multipath fading channels, causing deformation in the signal. To remove the effect (imposed by channel) from received signal, the receiver needs to have knowledge of CIR (Channel impulse response) that is usually provided by a separate channel estimator. This paper is aimed at exploring the use of Neural Network (NN) as a tool for MIMO-OFDM channel estimation and compensation. The research attempts to gauges the usefulness of proposed system by analyzing different algorithms to train NN. Further to ascertain the performance of the proposed technique; length of the known training sequence has been varied over a reasonable range and observations are made. Finally, the results obtained by using different algorithms for training NN have been compared with each-other and against the traditional least squares channel estimator, which along with observations/comments form part of the paper.