Online Regularization of Complex-Valued Neural Networks for Structure Optimization in Wireless-Communication Channel Prediction

This article proposes online-learning complex-valued neural networks (CVNNs) to predict future channel states in fast-fading multipath mobile communications. CVNN is suitable for dealing with a fading communication channel as a single complex-valued entity. This framework makes it possible to realize accurate channel prediction by utilizing its high generalization ability in the complex domain. However, actual communication environments are marked by rapid and irregular changes, thus causing fluctuation of communication channel states. Hence, an empirically selected stationary network gives only limited prediction accuracy. In this article, we introduce regularization in updates of the CVNN weights to develop online dynamics that can self-optimize its effective network size in response to such channel-state changes. It realizes online adaptive, highly accurate and robust channel prediction with dynamical adjustment of the network size. We characterize its online adaptability in a series of simulations and our practical wireless-propagation experiments demonstrate that the proposed channel prediction scheme provides 2.5 dB and 5.5 dB improvement of bit error rate (BER) at 10
^-3
and $5\times 10^{-4}$ , and achieves 10
^-5
BER with $E_{b}/N_{0}=23-24$ dB.