{"title":"Low-Rank Matrix Sensing-Based Channel Estimation for mmWave and THz Hybrid MIMO Systems","authors":"Khawaja Fahad Masood;Jun Tong;Jiangtao Xi;Jinhong Yuan;Qinghua Guo;Yanguang Yu","doi":"10.1109/JSTSP.2023.3288703","DOIUrl":null,"url":null,"abstract":"This article studies the channel estimation for wideband multiple-input multiple-output (MIMO) systems equipped with hybrid analog/digital transceivers operating in the millimeter-wave (mmWave) or terahertz (THz) bands. By exploiting the low-rank property of the concatenated channel matrix of the delay taps, we formulate the channel estimation problem as a low-rank matrix sensing (LRMS) problem and solve it using a low-complexity generalized conditional gradient-alternating minimization (GCG-ALTMIN) algorithm. This LRMS-based solution can accommodate different precoder/combiner and training structures. In addition, it does not require knowledge about the array responses at the transceivers, in contrast to most existing solutions allowing low training overhead. Furthermore, a preconditioned conjugate gradient (PCG) algorithm-based implementation and a low-rank matrix completion (LRMC) formulation are proposed to further reduce the computational complexity. In order to enhance the channel estimation performance for fat and tall channel matrices, we introduce a matrix reshaping approach that can preserve the channel rank by exploiting the shift-invariance property of uniform arrays. We also introduce a spectrum denoising (SD) approach for further improving the performance when the array responses are known and the number of paths is small. These approaches can effectively enhance the performance at a given training overhead. Simulation results suggest that the proposed solutions can achieve higher channel estimation accuracy and reduce the computational complexity as compared to several representative channel estimation schemes.","PeriodicalId":13038,"journal":{"name":"IEEE Journal of Selected Topics in Signal Processing","volume":null,"pages":null},"PeriodicalIF":8.7000,"publicationDate":"2023-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Signal Processing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10159514/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
This article studies the channel estimation for wideband multiple-input multiple-output (MIMO) systems equipped with hybrid analog/digital transceivers operating in the millimeter-wave (mmWave) or terahertz (THz) bands. By exploiting the low-rank property of the concatenated channel matrix of the delay taps, we formulate the channel estimation problem as a low-rank matrix sensing (LRMS) problem and solve it using a low-complexity generalized conditional gradient-alternating minimization (GCG-ALTMIN) algorithm. This LRMS-based solution can accommodate different precoder/combiner and training structures. In addition, it does not require knowledge about the array responses at the transceivers, in contrast to most existing solutions allowing low training overhead. Furthermore, a preconditioned conjugate gradient (PCG) algorithm-based implementation and a low-rank matrix completion (LRMC) formulation are proposed to further reduce the computational complexity. In order to enhance the channel estimation performance for fat and tall channel matrices, we introduce a matrix reshaping approach that can preserve the channel rank by exploiting the shift-invariance property of uniform arrays. We also introduce a spectrum denoising (SD) approach for further improving the performance when the array responses are known and the number of paths is small. These approaches can effectively enhance the performance at a given training overhead. Simulation results suggest that the proposed solutions can achieve higher channel estimation accuracy and reduce the computational complexity as compared to several representative channel estimation schemes.
期刊介绍:
The IEEE Journal of Selected Topics in Signal Processing (JSTSP) focuses on the Field of Interest of the IEEE Signal Processing Society, which encompasses the theory and application of various signal processing techniques. These techniques include filtering, coding, transmitting, estimating, detecting, analyzing, recognizing, synthesizing, recording, and reproducing signals using digital or analog devices. The term "signal" covers a wide range of data types, including audio, video, speech, image, communication, geophysical, sonar, radar, medical, musical, and others.
The journal format allows for in-depth exploration of signal processing topics, enabling the Society to cover both established and emerging areas. This includes interdisciplinary fields such as biomedical engineering and language processing, as well as areas not traditionally associated with engineering.