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

Xiangxue Ma,Shuaishuai Guo,Dongfeng Yuan

doi:10.1109/access.2020.2987055

Xiangxue Ma, Shuaishuai Guo + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2987055

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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.

Highlights

Massive machine-type communication [1] is a crucial communication challenge for the generation wireless systems to support a variety of Internet-of-Things (IoT) applications [2]
TYPE I: STATIC USER ACTIVATION Media-based modulation (MBM)-ENABLED Massive machine-type communication (mMTC) NETWORK Based on the proposed PIA-MSMP algorithm in TYPE I, Fig. 5 an 6 demonstrate the performance comparison result with the existing compressed sensing (CS)-based multiuser detection schemes by considering two performance measures: BER and RA
By utilizing sporadic user activation, sparse MBM signal and temporal correlation, the transmission schemes are divided into fixed user activation and dynamic user activation where the corresponding improved CS-based PIA-MSMP and PIA-ADMSMP algorithms are proposed, respectively

Summary

INTRODUCTION

Massive machine-type communication (mMTC) [1] is a crucial communication challenge for the generation wireless systems to support a variety of Internet-of-Things (IoT) applications [2]. The papers [15]–[22] only consider the sparsity nature of index modulation schemes [15] (including MBM and spatial modulation) where all the users participate in the transmission Both point to point system [16], [17] and multiuser system [18], [19] adopt such sparse characteristic to design CS-based detection, yet the number of TAs or RF mirrors in the existing CS-based detection schemes need sufficiently large to maintain the effective sparse signal recovery performance. In [22], the proposed enhanced structured block-sparse compressive sampling matching pursuit (ESBCoSaMP) algorithm considers both sporadic user identification and SM signal detection in one-time slot without utilizing the temporal correlation.

SYSTEM MODEL

TYPE I

TYPE II

Findings

CONCLUSION