Channel prediction for OFDMA using mixtures of experts

Abstract

Channel prediction is the key requirement in adaptive transmission techniques such as adaptive modulation, adaptive coding and adaptive power control. This paper presents a novel self organizing map (SOM) based channel predictor for the downlink of an orthogonal frequency-division multiple access (OFDMA) system. The proposed predictor uses a Kalman trained-SOM backed mixtures of experts (ME) modular neural network. The performance of the predictor is evaluated on an OFDMA system with a system delay where a channel prediction is needed.