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

TL;DR

This paper proposes a novel compressive sensing-based joint activity detection and channel estimation scheme for massive IoT access using multi-panel massive MIMO systems at mmWave/THz frequencies, leveraging structured sparsity.

Contribution

It introduces an EM-assisted OAMP algorithm tailored for nonuniform multi-panel MIMO arrays, enhancing AUD and CE performance in massive IoT scenarios.

Findings

Improved activity detection accuracy over conventional methods

Enhanced channel estimation precision in mmWave/THz systems

Effective handling of nonuniform array structures

Abstract

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.