Improved Compressed Sensing-Based Joint User and Symbol Detection for Media-Based Modulation-Enabled Massive Machine- Type Communications

Abstract

Media-Based Modulation (MBM) is regarded as a promising technique for future massive machine-type communications (mMTC) due to its high energy/spectral efficiency, good error performance and low-complexity radio frequency hardware implementation. In this paper, we consider both sparsity nature of user activity and sparsity nature of MBM signals in the uplink MBM-enabled mMTC system. According to the static user activation or the dynamic user activation in a coherent time, we classify the transmission schemes into two types and propose corresponding improved compressive sensing (CS)-based joint user identification and data detection with/without prior information of channel state information (CSI). The simulation results demonstrate the performance advantages of our proposed algorithms over the state-of-the-art CS-based user detection methods or CS-based symbol detection methods and evaluate the performance with different system parameters.

Keywords

• Massive machine-type communications (mMTC)
• media-based modulation (MBM)
• compressive sensing