arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2209.04846cs.ITmath.IT
收藏

基于毫米波/太赫兹多面板Massive MIMO的Massive IoT接入联合活动检测和信道估计

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

Hanlin Xiu, Zhen Gao, Anwen Liao, Yikun Mei, Dezhi Zheng, Shufeng Tan, Marco Di Renzo, Lajos Hanzo

摘要:

多面板阵列作为一种最先进的天线封装技术,由于其低成本部署和可扩展配置,非常适合毫米波(mmWave)/太赫兹(THz)系统。但在非均匀阵列结构的情况下,它会导致棘手的信号处理。基于这样的阵列结构在基站,本文研究了一种联合主动用户检测(AUD)和信道估计(CE)方案的压缩感知(CS)的应用于大规模物联网(IoT)。具体而言,通过利用mmWave/THz大规模物联网接入信道的结构化稀疏性,我们首先将基于多面板大规模多输入多输出(mMIMO)的联合AUD和CE问题表示为多测量向量(MMV)-CS问题。然后,我们利用期望最大化(EM)算法来学习先验参数(即,噪声方差和稀疏比),提出了一种正交近似消息传递(OAMP)-EM-MMV算法。我们的仿真结果验证了改进的AUD和CE性能相比,传统的CS为基础的算法所提出的计划。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑