Low complexity channel prediction using TFOS-ELM method for massive MIMO systems

Abstract

Multiple-input multiple-output (MIMO) technology can potentially help to achieve high data rates for multiuser communication. To achieve better performance, the channel state information (CSI) is estimated by the pilot. However, the estimated CSI cannot be used in downlinks when the mobile speed is very high, since it becomes outdated due to the rapid channel variation. In a massive MIMO system, the issue of outdated CSI is serious when using traditional techniques. Therefore, in order to obtain accurate CSI, the prediction of future CSI is required. In this paper, a low complexity online extreme learning machine (ELM) is proposed for the online prediction of the fast fading channel. First, we introduce the structure of the online sequential extreme learning machine (OS-ELM) and combine the training process of the OS-ELM with a forgetting mechanism (FM) to predict fast changing channels. Second, we use the truncated polynomial expansion (TPE) to reduce the computational complexity of the OS-ELM with the FM (FOS-ELM). In addition, the performance of the proposed algorithm is verified through simulation results, and we apply channel prediction in the precoding process. It is shown that the communication quality is improved by our channel prediction algorithm.