论文标题
基于毫米波/Terahertz多面板大量MIMO的联合活动检测和通道估计
Joint Activity Detection and Channel Estimation for Massive IoT Access Based on Millimeter-Wave/Terahertz Multi-Panel Massive MIMO
论文作者
论文摘要
由于其低成本的部署和可扩展的配置，因此多面板阵列是一种最先进的包装技术，非常适合毫米波（MMWave）/Terahertz（THZ）系统。但是在不均匀阵列结构的背景下，它导致了棘手的信号处理。基于基站的这种阵列结构，本文根据压缩传感（CS）研究了联合主动用户检测（AUD）和通道估计（CE）方案，以应用于大规模的物联网（IoT）。具体而言，通过利用MMWave/THZ大量IoT访问通道的结构化稀疏性，我们首先制定了多层大量多输入多输入（MMIMO）基于多个测量矢量（MMV）的关节AUD（MMV）-CS问题。然后，我们利用期望最大化（EM）算法来学习先前的参数（即噪声方差和稀疏性比）和正交近似消息传递（OAMP）-EM-MMV算法是为解决此问题而开发的。我们的仿真结果与常规的基于CS的算法相比，验证了拟议方案的AUD和CE性能的提高。
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.
