This paper considers massive access in massive multiple-input multiple-output
(MIMO) systems and proposes an adaptive active user detection and channel
estimation scheme based on compressive sensing. By exploiting the sporadic
traffic of massive connected user equipments and the virtual angular domain
sparsity of massive MIMO channels, the proposed scheme can support massive
access with dramatically reduced access latency. Specifically, we design
non-orthogonal pseudo-random pilots for uplink broadband massive access, and
formulate the active user detection and channel estimation problems as a
generalized multiple measurement vector compressive sensing problem.
Furthermore, by leveraging the structured sparsity of the uplink channel
matrix, we propose an efficient generalized multiple measurement vector
approximate message passing (GMMV-AMP) algorithm to realize simultaneous active
user detection and channel estimation based on a spatial domain or an angular
domain channel model. To jointly exploit the channel sparsity presented in both
the spatial and the angular domains for enhanced performance, a Turbo-GMMV-AMP
algorithm is developed for detecting the active users and estimating their
channels in an alternating manner. Finally, an adaptive access scheme is
proposed, which adapts the access latency to guarantee reliable massive access
for practical systems with unknown channel sparsity level. Additionally, the
state evolution of the proposed GMMV-AMP algorithm is derived to predict its
performance. Simulation results demonstrate the superiority of the proposed
active user detection and channel estimation schemes compared to several
baseline schemes.

本文提出了一种基于压缩感知的自适应主动用户检测和信道估计方案，可支持出现突发流量的大规模连接用户设备和大规模 MIMO 信道的虚拟角度域稀疏，实现高效可靠的大规模信道接入。