Massive access in extra large-scale MIMO with mixed-ADC over near-field channels

Yikun Mei, Zhen Gao, De Mi, Mingyu Zhou, Dezhi Zheng, Michalis Matthaiou, Pei Xiao, Robert Schober

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
54 Downloads (Pure)


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.
Original languageEnglish
Number of pages6
JournalIEEE Transactions on Vehicular Technology
Early online date11 Apr 2023
Publication statusEarly online date - 11 Apr 2023


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