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.

翻译：针对近场接入信道和成本过高的问题，超大规模多输入多输出（XL-MIMO）系统的海量连接是一项具有挑战性的课题。本文提出了一种面向XL-MIMO系统的上行免授权海量接入方案，该方案采用混合模数转换器（ADC）架构，以在接入性能与功耗之间取得平衡。通过利用海量接入信道的空间域结构化稀疏性和分段角度域簇稀疏性，本文提出了一种基于压缩感知（CS）的两阶段正交近似消息传递算法，以高效解决联合活跃度检测与信道估计问题。特别地，利用高精度量化测量值进行精确的超参数估计，从而促进活跃度检测。此外，采用子阵列逐次估计策略来克服XL-MIMO信道中近场效应引起的严重角度域能量弥散问题。仿真结果验证了所提算法在基于混合ADC架构的XL-MIMO海量接入场景中优于现有最优CS算法。