Deep Unfolding Scheme for Grant-Free Massive-Access Vehicular Networks
Journal Publication ResearchOnline@JCUAbstract
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.
Journal
IEEE Transactions on Intelligent Transportation Systems
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Volume
24
ISBN/ISSN
1558-0016
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Issue
12
Pages Count
10
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Publisher
Institute of Electrical and Electronics Engineers (IEEE)
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Date
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EISSN
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DOI
10.1109/TITS.2023.3296452