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

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 non-uniform 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.

Automated summary

This paper investigates a joint active user detection (AUD) and channel estimation (CE) scheme based on compressive sensing (CS) for millimeter-wave (mmWave)/terahertz (THz) systems in the context of non-uniform array structures. By exploiting the structured sparsity of mmWave/THz massive IoT access channels, the authors formulate the problem as a multiple measurement vector (MMV)-CS problem and develop an orthogonal approximate message passing (OAMP)-EM-MMV algorithm to solve it. Simulation results show the improved performance of the proposed scheme compared to conventional CS-based algorithms.