A Comparison of Neural Networks for Wireless Channel Prediction
Oscar Stenhammar Gabor Fodor Carlo Fischione
The performance of modern wireless communications systems depends critically
on the quality of the available channel state information (CSI) at the
transmitter and receiver. Several previous works have proposed concepts and
algorithms that help maintain high quality CSI even in the presence of high
mobility and channel aging, such as temporal prediction schemes that employ
neural networks. However, it is still unclear which neural network-based scheme
provides the best performance in terms of prediction quality, training
complexity and practical feasibility. To investigate such a question, this
paper first provides an overview of state-of-the-art neural networks applicable
to channel prediction and compares their performance in terms of prediction
quality. Next, a new comparative analysis is proposed for four promising neural
networks with different prediction horizons. The well-known tapped delay
channel model recommended by the Third Generation Partnership Program is used
for a standardized comparison among the neural networks. Based on this
comparative evaluation, the advantages and disadvantages of each neural network
are discussed and guidelines for selecting the best-suited neural network in
channel prediction applications are given.