  This paper 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 paper, we introduce regularization in the update of the CVNN
weights to develop online dynamics that can self-optimize its effective network
size by responding to such channel-state changes. It realizes online adaptive,
highly accurate and robust channel prediction with dynamical adjustment of the
network size. We demonstrate its online adaptability in simulations and real
wireless-propagation experiments.
