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Compressive Sensing Based Grant-Free Random Access for Massive MTC

Mei, Yikun, Gao, Zhen, Mi, De, Xiao, Pei and Alouini, Mohamed-Slim (2020) Compressive Sensing Based Grant-Free Random Access for Massive MTC In: IEEE International Conference on UK-China Emerging Technologies (UCET) 2020, 20-21 Aug 2020, Glasgow, Scotland, UK.

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Massive machine-type communications (mMTC) are expected to be one of the most primary scenarios in the next-generation wireless communications and provide massive connectivity for Internet of Things (IoT). To meet the demanding technical requirements for mMTC, random access scheme with efficient joint activity and data detection (JADD) is vital. In this paper, we propose a compressive sensing (CS)-based grant-free random access scheme for mMTC, where JADD is formulated as a multiple measurement vectors (MMV) CS problem. By leveraging the prior knowledge of the discrete constellation symbols, we develop an orthogonal approximate message passing (OAMP)-MMV algorithm for JADD, where the structured sparsity is fully exploited for enhanced performance. Moreover, expectation maximization (EM) algorithm is employed to learn the unknown sparsity ratio of the a priori distribution and the noise variance. Simulation results show that the proposed scheme achieves superior performance over other state-of-the-art CS schemes.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
Mei, Yikun
Gao, Zhen
Alouini, Mohamed-Slim
Date : 2020
Copyright Disclaimer : © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Compressive sensing; Massive machine-type communications; Orthogonal approximate message passing; Multiple measurement vectors
Related URLs :
Depositing User : Clive Harris
Date Deposited : 07 Sep 2020 11:01
Last Modified : 07 Sep 2020 11:01

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