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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 toits low-cost deployment and scalable configuration. But in the context ofnonuniform array structures it leads to intractable signal processing. Based onsuch an array structure at the base station, this paper investigates a jointactive user detection (AUD) and channel estimation (CE) scheme based oncompressive sensing (CS) for application to the massive Internet of Things(IoT). Specifically, by exploiting the structured sparsity of mmWave/THzmassive IoT access channels, we firstly formulate the multi-panel massivemultiple-input multiple-output (mMIMO)-based joint AUD and CE problem as amultiple measurement vector (MMV)-CS problem. Then, we harness the expectationmaximization (EM) algorithm to learn the prior parameters (i.e., the noisevariance and the sparsity ratio) and an orthogonal approximate message passing(OAMP)-EM-MMV algorithm is developed to solve this problem. Our simulationresults verify the improved AUD and CE performance of the proposed schemecompared to conventional CS-based algorithms.

Joint Activity Detection and Channel Estimation for Massive IoT Access Based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO