Dang, Xiaobing, Xiang, Wei, Yuan, Lei, Yang, Yuan, Wang, Eric, and Huang, Tao (2023) Deep Unfolding Scheme for Grant-Free Massive-Access Vehicular Networks. IEEE Transactions on Intelligent Transportation Systems, 24 (12). pp. 14443-14452.

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Abstract

Grant-free random access is an effective solution to enable massive access for future Internet of Vehicles (IoV) scenarios based on massive machine-type communication (mMTC). Considering the uplink transmission of grant-free based vehicular networks, vehicular devices sporadically access the base station, the joint active device detection (ADD) and channel estimation (CE) problem can be addressed by compressive sensing (CS) recovery algorithms due to the sparsity of transmitted signals. However, traditional CS-based algorithms present high complexity and low recovery accuracy. In this manuscript, we propose a novel alternating direction method of multipliers (ADMM) algorithm with low complexity to solve this problem by minimizing the ℓ2,1 norm. Furthermore, we design a deep unfolded network with learnable parameters based on the proposed ADMM, which can simultaneously improve convergence rate and recovery accuracy. The experimental results demonstrate that the proposed unfolded network performs better performance than other traditional algorithms in terms of ADD and CE.

Item ID:	80777
Item Type:	Article (Research - C1)
ISSN:	1558-0016
Keywords:	Grant-free, mMTC, IoV, compressive sensing, alternating direction method of multipliers, deep unfolding
Copyright Information:	© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
Date Deposited:	31 Oct 2023 01:22
FoR Codes:	40 ENGINEERING > 4006 Communications engineering > 400608 Wireless communication systems and technologies (incl. microwave and millimetrewave) @ 50% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning @ 50%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2201 Communication technologies, systems and services > 220107 Wireless technologies, networks and services @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50%
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