Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1‑Bit ADCs and Oversampling

doi:10.12142/ZTECOM.202201007

ZTE Communications ›› 2022, Vol. 20 ›› Issue (1): 48-56.DOI: 10.12142/ZTECOM.202201007

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Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1‑Bit ADCs and Oversampling

SHAO Zhichao, YAN Wenjing, YUAN Xiaojun()

National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China

Received:2021-12-27 Online:2022-03-25 Published:2022-04-06
About author:SHAO Zhichao received the B.S. degree in information engineering from Xidian University, China in 2012, the M.S. degree in electrical engineering from Technische Universit?t Dresden, Germany in 2016, and the Ph.D. degree in electrical engineering from the Pontifical Catholic University of Rio de Janeiro, Brazil in 2020. Currently, he is a post-doctoral researcher with the National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China (UESTC). His research interests lie in communications and signal processing.|YAN Wenjing received the B.S. degree from Chongqing University, China in 2018, and the M.S. degree from University of Electronic Science and Technology of China (UESTC) in 2021. She is currently pursuing the Ph.D. degree with the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology (HKUST), China. Her contribution to the work in this paper was done when she was with UESTC.|YUAN Xiaojun (xjyuan@uestc.edu.cn) received the Ph.D. degree in electrical engineering from the City University of Hong Kong, China in 2009. From 2009 to 2011, he was a research fellow with the Department of Electronic Engineering, City University of Hong Kong. He was also a visiting scholar with the Department of Electrical Engineering, University of Hawaii at Manoa, USA in spring and summer 2009, as well as in the same period of 2010. From 2011 to 2014, he was a research assistant professor with the Institute of Network Coding, The Chinese University of Hong Kong, China. From 2014 to 2017, he was an assistant professor with the School of Information Science and Technology, Shanghai Tech University, China. He is currently a State-Specially-Recruited Professor with the Center for Intelligent Networking and Communications, University of Electronic Science and Technology of China. His research interests include signal processing, machine learning, and wireless communications, including but not limited to multi-antenna and cooperative communications, sparse and structured signal recovery, Bayesian approximate inference, and network coding. He has published more than 180 peer-reviewed research articles in the leading international journals and conferences in the related areas. He has served on a number of technical programs for international conferences. He was a co-recipient of the Best Paper Award of IEEE International Conference on Communications (ICC) 2014 and a co-recipient of the Best Journal Paper Award of IEEE Technical Committee on Green Communications and Computing (TCGCC) 2017. He has been an editor of IEEE Transactions on Communications since 2017 and IEEE Transactions on Wireless Communications since 2018.

Abstract

Abstract:

A reconfigurable intelligent surface (RIS) aided massive multiple-input multiple-output (MIMO) system is considered, where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters (ADCs). To compensate for the performance loss caused by the coarse quantization, oversampling is applied at the receiver. The main challenge for the acquisition of cascaded channel state information in such a system is to handle the distortion caused by the 1-bit quantization and the sample correlation caused by oversampling. In this work, Bussgang decomposition is applied to deal with the coarse quantization, and a Markov chain is developed to characterize the banded structure of the oversampling filter. An approximate message-passing based algorithm is proposed for the estimation of the cascaded channels. Simulation results demonstrate that our proposed 1-bit systems with oversampling can approach the 2-bit systems in terms of the mean square error performance while the former consumes much less power at the receiver.

Key words: massive MIMO, reconfigurable intelligent surface, channel estimation, 1-bit ADCs, oversampling

SHAO Zhichao, YAN Wenjing, YUAN Xiaojun. Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1‑Bit ADCs and Oversampling[J]. ZTE Communications, 2022, 20(1): 48-56.

Figures/Tables 5

Figure 1 System model of RIS aided multi-user 1-bit massive MIMO system with oversampling at the receiver

Figure 2 Factor graph representation of Eq. (21) for an example with M=Nr=Nt=L=2 and N=3

Figure 3 NMSE of G and H comparisons between the multi-bit systems with Nyquist rate (M=1) and the 1-bit systems with multi-fold oversampling under uncorrelated channel

Figure 4 NMSE of G and H comparisons between the multi-bit systems with Nyquist rate (M=1) and the 1-bit systems with multi-fold oversampling under correlated channel

Figure 5 Simplified power consumption at the receiver as a function of the number of quantization bits

References 18

1	LARSSON E G, EDFORS O, TUFVESSON F, et al. Massive MIMO for next generation wireless systems [J]. IEEE communications magazine, 2014, 52(2): 186–195. DOI: 10.1109/MCOM.2014.6736761 DOI
2	LIASKOS C, NIE S, TSIOLIARIDOU A, et al. A new wireless communication paradigm through software-controlled metasurfaces [J]. IEEE communications magazine, 2018, 56(9): 162–169. DOI: 10.1109/MCOM.2018.1700659 DOI
3	JAMALI V, TULINO A M, FISCHER G, et al. Intelligent surface-aided transmitter architectures for millimeter-wave ultra massive MIMO systems [J]. IEEE open journal of the communications society, 2020, 2: 144–167. DOI: 10.1109/OJCOMS.2020.3048063 DOI
4	TAHA A, ALRABEIAH M, ALKHATEEB A. Enabling large intelligent surfaces with compressive sensing and deep learning [J]. IEEE access, 2021, 9: 44304–44321. DOI: 10.1109/ACCESS.2021.3064073 DOI
5	ARDAH K, GHEREKHLOO S, DE ALMEIDA A L F, et al. TRICE: A channel estimation framework for RIS-aided millimeter-wave MIMO systems [J]. IEEE signal processing letters, 2021, 28: 513–517. DOI: 10.1109/LSP.2021.3059363 DOI
6	HE Z Q, YUAN X J. Cascaded channel estimation for large intelligent metasurface assisted massive MIMO [J]. IEEE wireless communications letters, 2020, 9(2): 210–214. DOI: 10.1109/LWC.2019.2948632 DOI
7	WANG P L, FANG J, DUAN H P, et al. Compressed channel estimation for intelligent reflecting surface-assisted millimeter wave systems [J]. IEEE signal processing letters, 2020, 27: 905–909. DOI: 10.1109/LSP.2020.2998357 DOI
8	HE Z Q, LIU H, YUAN X J, et al. Semi-blind cascaded channel estimation for reconfigurable intelligent surface aided massive MIMO [EB/OL]. [2021-11-15].
9	WALDEN R H. Analog-to-digital converter survey and analysis [J]. IEEE journal on selected areas in communications, 1999, 17(4): 539–550. DOI: 10.1109/49.761034 DOI
10	MO J H, HEATH R W. Capacity analysis of one-bit quantized MIMO systems with transmitter channel state information [J]. IEEE transactions on signal processing, 2015, 63(20): 5498–5512. DOI: 10.1109/TSP.2015.2455527 DOI
11	LI Y Z, TAO C, SECO-GRANADOS G, et al. Channel estimation and performance analysis of one-bit massive MIMO systems [J]. IEEE transactions on signal processing, 2017, 65(15): 4075–4089. DOI: 10.1109/TSP.2017.2706179 DOI
12	SHAO Z C, DE LAMARE R C, LANDAU L T N. Iterative detection and decoding for large-scale multiple-antenna systems with 1-bit ADCs [J]. IEEE wireless communications letters, 2018, 7(3): 476–479. DOI: 10.1109/LWC.2017.2787159 DOI
13	SHAO Z C, LANDAU L T N, DE LAMARE R C. Channel estimation for large-scale multiple-antenna systems using 1-bit ADCs and oversampling [J]. IEEE access, 2020, 8: 85243–85256. DOI: 10.1109/ACCESS.2020.2992246 DOI
14	SHAO Z C, LANDAU L T N, DE LAMARE R C. Dynamic oversampling for 1-bit ADCs in large-scale multiple-antenna systems [J]. IEEE transactions on communications, 2021, 69(5): 3423–3435. DOI: 10.1109/TCOMM.2021.3059303 DOI
15	BUSSGANG J J. Crosscorrelation functions of amplitude-distorted Gaussian signals [R]. Cambridge, USA: Massachusetts Institute of Technology, 1952
16	JACOVITTI G, NERI A. Estimation of the autocorrelation function of complex Gaussian stationary processes by amplitude clipped signals [J]. IEEE transactions on information theory, 1994, 40(1): 239–245. DOI: 10.1109/18.272490 DOI
17	WEN C K, WANG C J, JIN S, et al. Bayes-optimal joint channel-and-data estimation for massive MIMO with low-precision ADCs [J]. IEEE transactions on signal processing, 2016, 64(10): 2541–2556. DOI: 10.1109/TSP.2015.2508786 DOI
18	ROTH K, NOSSEK J A. Achievable rate and energy efficiency of hybrid and digital beamforming receivers with low resolution ADC [J]. IEEE journal on selected areas in communications, 2017, 35(9): 2056–2068. DOI: 10.1109/JSAC.2017.2720398 DOI

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Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1‑Bit ADCs and Oversampling

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