Massive Access in Extra Large-Scale MIMO With Mixed-ADC Over Near-Field Channels

Mei, Yikun and Gao, Zhen and Mi, De and Zhou, Mingyu and Zheng, Dezhi and Matthaiou, Michail and Xiao, Pei and Schober, Robert (2023) Massive Access in Extra Large-Scale MIMO With Mixed-ADC Over Near-Field Channels. IEEE Transactions on Vehicular Technology, 72 (9). pp. 12373-12378. ISSN 0018-9545

2207.01983v2.pdf - Accepted Version

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Massive connectivity for extra large-scale multi-input multi-output (XL-MIMO) systems is a challenging issue due to the near-field access channels and the prohibitive cost. In this paper, we propose an uplink grant-free massive access scheme for XL-MIMO systems, in which a mixed-analog-to-digital converters (ADC) architecture is adopted to strike the right balance between access performance and power consumption. By exploiting the spatial-domain structured sparsity and the piecewise angular-domain cluster sparsity of massive access channels, a compressive sensing (CS)-based two-stage orthogonal approximate message passing algorithm is proposed to efficiently solve the joint activity detection and channel estimation problem. Particularly, high-precision quantized measurements are leveraged to perform accurate hyper-parameter estimation, thereby facilitating the activity detection. Moreover, we adopt a subarray-wise estimation strategy to overcome the severe angular-domain energy dispersion problem which is caused by the near-field effect in XL-MIMO channels. Simulation results verify the superiority of our proposed algorithm over state-of-the-art CS algorithms for massive access based on XL-MIMO with mixed-ADC architectures.

Item Type: Article
Identification Number:
1 April 2023Accepted
11 April 2023Published Online
Uncontrolled Keywords: Compressive sensing, massive access, mixed-ADC, orthogonal approximate message passing, XL-MIMO system
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Gemma Tonks
Date Deposited: 06 Jun 2024 12:41
Last Modified: 06 Jun 2024 12:41

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