Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications

TL;DR

This paper introduces a compressive sensing approach combined with media modulation to improve massive device detection and data decoding in IoT scenarios, leveraging sparsity for enhanced performance.

Contribution

It proposes a novel compressive sensing framework with specialized algorithms for active device detection and data demodulation in media modulation aided mMTC, improving detection accuracy.

Findings

Outperforms existing solutions in detection accuracy.

Utilizes structured sparsity for better signal recovery.

Demonstrates robustness in large-scale IoT scenarios.

Abstract

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a block sparsity adaptive matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.