DEC-aided SM-OFDM: A Spatial Modulation System with Deep Learning based Error Correction

Abstract

In this work, we propose a Deep Learning (DL) based error correction system termed as DEC. It predicts the transmitted symbols at the receiver using the received soft symbols and channel state information (CSI) of the transmission link. Hence, the proposed system eliminates the need of using complex channel coding/decoding blocks in the wireless communication system. Specifically, we explore the application of proposed DEC system for Spatial Modulation-OFDM (SM-OFDM) systems. SM is a technique that avoids inter-channel interference (ICI) at receiver input, also offers a good balance between the energy and spectral efficiency. This together with DEC system can prove to be of interest for the next generation wireless system, particularly for the Internet-of-Things (IoT) devices that require optimal bit-error ratios (BER) at moderate data rates. The performance of the proposed system is compared with Trellis coded-SM (TCSM) system. The obtained simulation results successfully verify the superiority of the DEC-aided SM-OFDM system over the TCSM in terms of both BER and throughput.