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. INDEX TERMS Massive machine-type communications (mMTC), media-based modulation (MBM), compressive sensing.

Joint Activity and Blind Information Detection for UAV-Assisted Massive IoT Access

“…In the second stage, data is transmitted and coherent data detection (DD) is performed at the BS based on the previously estimated channel state information (CSI). We refer to this scheme as coherent GFRA type I, as shown Common Device Activity in Multiple Time-Slots [12], [13], [15], [23] [15]…”

“…To further improve the spectral and energy efficiency of IoT devices, coherent grant-free massive IoT access relying on index modulation (IM) has been considered in more recent works, e.g., [20]- [23]. In fact, IM is a highly spectraland energy-efficient yet simple digital modulation technique, which utilizes the indices of pre-defined codebooks to convey additional information bits [27], [28].…”

“…[29]- [32]. Specifically, the coherent GFRA type II scheme with SM and media modulation are investigated in [20] and [21]- [23], respectively. The authors of [20] proposed a two-level sparse structure greedy algorithm to detect the spatially modulated symbols at the receiver with perfectly known CSI.…”

“…The authors of [20] proposed a two-level sparse structure greedy algorithm to detect the spatially modulated symbols at the receiver with perfectly known CSI. By exploiting the sporadic traffic in IoT devices and the structured sparsity of media modulated symbols, greedy algorithms were proposed in [21], [23], and an AMP-based algorithm was developed in [22] for JDAD-DD with perfectly known CSI. Also, with the consideration of the finite alphabet constraint of the transmitted symbols, it was shown that the algorithm proposed in [22] outperforms those in [21] and [23].…”

“…By exploiting the sporadic traffic in IoT devices and the structured sparsity of media modulated symbols, greedy algorithms were proposed in [21], [23], and an AMP-based algorithm was developed in [22] for JDAD-DD with perfectly known CSI. Also, with the consideration of the finite alphabet constraint of the transmitted symbols, it was shown that the algorithm proposed in [22] outperforms those in [21] and [23]. Furthermore, the authors of [22] proposed a data-aided CE strategy, which can reduce the channel training overhead in media modulation-based massive access.…”