This paper considers sparse device activity detection for cellular
machine-type communications with non-orthogonal signatures using the
approximate message passing algorithm. This paper compares two network
architectures, massive multiple-input multiple-output (MIMO) and cooperative
MIMO, in terms of their effectiveness in overcoming inter-cell interference. In
the massive MIMO architecture, each base station (BS) detects only the users
from its own cell while treating inter-cell interference as noise. In the
cooperative MIMO architecture, each BS detects the users from neighboring cells
as well; the detection results are then forwarded in the form of log-likelihood
ratio (LLR) to a central unit where final decisions are made. This paper
analytically characterizes the probabilities of false alarm and missed
detection for both architectures. Numerical results validate the analytic
characterization and show that as the number of antennas increases, a massive
MIMO system effectively drives the detection error to zero, while as the
cooperation size increases, the cooperative MIMO architecture mainly improves
the cell-edge user performance. Moreover, this paper studies the effect of LLR
quantization to account for the finite-capacity fronthaul. Numerical
simulations of a practical scenario suggest that in that specific case
cooperating three BSs in a cooperative MIMO system achieves about the same
cell-edge detection reliability as a non-cooperative massive MIMO system with
four times the number of antennas per BS.

本研究考虑使用近似信息传递算法来实现非正交签名的移动通信中的稀疏设备活动检测。本文比较了两种网络架构：大规模多输入多输出（MIMO）和合作 MIMO，通过分析与数值模拟结果验证，大规模 MIMO 系统能有效将检测误差降至零，而增加合作规模主要能改善边缘用户性能，并探讨了 LLR 量化的影响。