Autoencoder-Based Spatial Modulation for the Next Generation of Wireless Networks

Abstract

Spatial modulation (SM) has been proposed as a multiple-input-multiple-output (MIMO)-based technique to overcome the interchannel interference experienced in conventional MIMO systems. It has been further shown that SM enhances energy efficiency and reduces the receiver's complexity. Nevertheless, under high antenna correlation scenarios, the detection performance of the antenna indices degrades significantly. To address this critical concern, in this article, we propose three autoencoder-based frameworks for SM. The first scenario, similar to conventional SM, trains the encoder for data modulation and the decoder for data demodulation as well as antenna index detection. The performance of this framework deteriorates in high antenna correlation scenarios. Therefore, two novel solutions are presented to embed the antenna index into the transmitted signal in order to reduce the receiver's reliance on the channel conditions. The first framework adds a phase-shift keying-based antenna signature, while the other trains the encoder to learn an appropriate antenna index embedding. Simulation results show that the two enhanced frameworks result in a significantly enhanced performance, compared to conventional SM, in terms of block error rate and power efficiency under a high correlation setup (about 18 and 24 dB gain, respectively, at a Rician factor of 20 dB).