Massive Access in Extra Large-Scale MIMO with Mixed-ADC over Near Field Channels

Massive connectivity for extra-large multi-input multi-output (XL-MIMO) systems is a challenging issue due to the prohibitive cost and the near-field non-stationary channels. 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 energy cost. 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 subarray-wise estimation strategy to overcome the severe angular-domain energy dispersion problem which is caused by the spatial non-stationarity of near-field 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.