Stacked Intelligent Metasurface-Aided MIMO Transceiver Design

被引:48
作者
An, Jiancheng [1 ]
Yuen, Chau [1 ]
Xu, Chao [2 ]
Li, Hongbin [4 ]
Ng, Derrick Wing Kwan [5 ]
Di Renzo, Marco [6 ]
Debbah, Merouane [7 ]
Hanzo, Lajos [3 ]
机构
[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore
[2] Univ Southampton, Next Generat Wireless Res Grp, Southampton, England
[3] Univ Southampton, Southampton, England
[4] Stevens Inst Technol, Dept Elect & Comp Engn, Hoboken, NJ USA
[5] Univ New South Wales, Sydney, Australia
[6] Univ Paris Saclay, Orsay, France
[7] Khalifa Univ Sci & Technol, Abu Dhabi, U Arab Emirates
基金
美国国家科学基金会; 英国工程与自然科学研究理事会; 欧洲研究理事会; 澳大利亚研究理事会;
关键词
MIMO communication; Metasurfaces; Transceivers; Computer architecture; Hardware; Array signal processing; Signal processing; SYSTEMS;
D O I
10.1109/MWC.013.2300259
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Next-generation wireless networks are expected to utilize limited radio frequency (RF) resources more efficiently with the aid of intelligent transceivers. To this end, we propose a promising transceiver architecture relying on stacked intelligent metasurfaces (SIM). An SIM is constructed by stacking an array of programmable metasurface layers, where each layer consists of a massive number of low-cost passive meta-atoms that individually manipulate the electromagnetic (EM) waves. By appropriately configuring the passive meta-atoms, an SIM is capable of accomplishing advanced computation and signal processing tasks, such as multiple-input multiple-output (MIMO) precoding/ combining, multi-user interference mitigation, and radar sensing, as the EM wave propagates through the multiple layers of the metasurface, which effectively reduces both the RF-related energy consumption and processing delay. Inspired by this, we provide an overview of the SIM-aided MIMO transceiver design, which encompasses its hardware architecture and its potential benefits over state-of-the-art solutions. Furthermore, we discuss promising application scenarios and identify the open research challenges associated with the design of advanced SIM architectures for next-generation wireless networks. Finally, numerical results are provided for quantifying the benefits of wavebased signal processing in wireless systems.
引用
收藏
页码:123 / 131
页数:9
相关论文
共 15 条
[1]   MIMO Precoding and Combining Solutions for Millimeter-Wave Systems [J].
Alkhateeb, Ahmed ;
Mo, Jianhua ;
Gonzalez-Prelcic, Nuria ;
Heath, Robert W., Jr. .
IEEE COMMUNICATIONS MAGAZINE, 2014, 52 (12) :122-131
[2]  
An, 2024, PROC IEEE INT C COMM, P1
[3]  
An J, 2023, Paradigm Shift from Feature-Based Machine Learning to End-to-End Deep Residual Neural Networks for Pediatric Age Classification from 12-Lead ECG, P1, DOI [10.22489/CinC.2023.376, DOI 10.22489/CINC.2023.376]
[4]   Stacked Intelligent Metasurfaces for Efficient Holographic MIMO Communications in 6G [J].
An, Jiancheng ;
Xu, Chao ;
Ng, Derrick Wing Kwan ;
Alexandropoulos, George C. ;
Huang, Chongwen ;
Yuen, Chau ;
Hanzo, Lajos .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2023, 41 (08) :2380-2396
[5]   Low-Complexity Channel Estimation and Passive Beamforming for RIS-Assisted MIMO Systems Relying on Discrete Phase Shifts [J].
An, Jiancheng ;
Xu, Chao ;
Gan, Lu ;
Hanzo, Lajos .
IEEE TRANSACTIONS ON COMMUNICATIONS, 2022, 70 (02) :1245-1260
[6]   Holographic Communication Using Intelligent Surfaces [J].
Dardari, Davide ;
Decarli, Nicolo .
IEEE COMMUNICATIONS MAGAZINE, 2021, 59 (06) :35-41
[7]   Rate-Splitting Multiple Access and Its Interplay with Intelligent Reflecting Surfaces [J].
de Sena, Arthur S. ;
Nardelli, Pedro H. J. ;
da Costa, Daniel B. ;
Popovski, Petar ;
Papadias, Constantinos B. .
IEEE COMMUNICATIONS MAGAZINE, 2022, 60 (07) :52-57
[8]   Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead [J].
Di Renzo, Marco ;
Zappone, Alessio ;
Debbah, Merouane ;
Alouini, Mohamed-Slim ;
Yuen, Chau ;
de Rosny, Julien ;
Tretyakov, Sergei .
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2020, 38 (11) :2450-2525
[9]   All-optical machine learning using diffractive deep neural networks [J].
Lin, Xing ;
Rivenson, Yair ;
Yardimei, Nezih T. ;
Veli, Muhammed ;
Luo, Yi ;
Jarrahi, Mona ;
Ozcan, Aydogan .
SCIENCE, 2018, 361 (6406) :1004-+
[10]   A programmable diffractive deep neural network based on a digital-coding metasurface array [J].
Liu, Che ;
Ma, Qian ;
Luo, Zhang Jie ;
Hong, Qiao Ru ;
Xiao, Qiang ;
Zhang, Hao Chi ;
Miao, Long ;
Yu, Wen Ming ;
Cheng, Qiang ;
Li, Lianlin ;
Cui, Tie Jun .
NATURE ELECTRONICS, 2022, 5 (02) :113-122