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

TL;DR

This paper introduces a novel uplink grant-free massive access scheme for XL-MIMO systems with mixed-ADC architecture, leveraging structured sparsity and a two-stage message passing algorithm to improve activity detection and channel estimation in near-field channels.

Contribution

It proposes a CS-based two-stage orthogonal approximate message passing algorithm tailored for XL-MIMO with mixed-ADC, addressing near-field effects and energy dispersion issues.

Findings

Outperforms state-of-the-art CS algorithms in simulations.

Effectively estimates activity and channels with mixed-ADC architecture.

Overcomes near-field angular dispersion challenges.

Abstract

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.