  The multi-panel array, as a state-of-the-art antenna-in-package technology,
is very suitable for millimeter-wave (mmWave)/terahertz (THz) systems, due to
its low-cost deployment and scalable configuration. But in the context of
nonuniform array structures it leads to intractable signal processing. Based on
such an array structure at the base station, this paper investigates a joint
active user detection (AUD) and channel estimation (CE) scheme based on
compressive sensing (CS) for application to the massive Internet of Things
(IoT). Specifically, by exploiting the structured sparsity of mmWave/THz
massive IoT access channels, we firstly formulate the multi-panel massive
multiple-input multiple-output (mMIMO)-based joint AUD and CE problem as a
multiple measurement vector (MMV)-CS problem. Then, we harness the expectation
maximization (EM) algorithm to learn the prior parameters (i.e., the noise
variance and the sparsity ratio) and an orthogonal approximate message passing
(OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulation
results verify the improved AUD and CE performance of the proposed scheme
compared to conventional CS-based algorithms.
