Recurrent Neural Network-based Channel Prediction in mMIMO for Enhanced Performance in Future Wireless Communication

Abstract

Massive MIMO (mMIMO) has been classified as one of the high potential future wireless communication technologies due to its unique abilities such as high user capacity, increased spectral density, and diversity. Due to the exponential increase of connected devices, these properties are critical for the current 5G-IoT era and future telecommunication networks. However, outdated channel state information (CSI) causes major performance degradation in mMIMO systems. Nevertheless, channel prediction using neural networks (NN) has gained tremendous attention as a way of mitigating outdated CSI. Hence, combined mMIMO and NN-based channel prediction is a revolutionary technology of future wireless communications. In this work, we review the current recurrent neural network-based (RNN-based) mMIMO channel prediction schemes and propose a low complexity, low cost channel prediction scheme.