Massive MIMO channel prediction using recurrent neural networks

Abstract

Massive MIMO has been classified as one of the high potential wireless communication technologies due to its unique abilities such as high user capacity, increased spectral density, and diversity among others. Due to the exponential increase of connected devices, these properties are of great importance for the current 5G-IoT era and future telecommunication networks. However, outdated channel state information (CSI) caused by the variations in the channel response due to the presence of highly mobile and rich scattering is a major problem facing massive MIMO systems. Outdated CSI occurs when the information obtained about the channel at the transmitter changes before transmission. This leads to performance degradation of the network. In this work, we demonstrate a low complexity channel prediction method using neural networks. Specifically, we explore the power of recurrent neural network utilizing long-short memory cells in analyzing time series data. We review various neural network-based channel prediction methods available in the literature and compare complexity and performance metrics. Results indicate that the proposed methods outperform conventional systems by tremendously lowering the complexity associated with channel prediction.