Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems

doi:10.12142/ZTECOM.202401004

ZTE Communications ›› 2024, Vol. 22 ›› Issue (1): 24-33.DOI: 10.12142/ZTECOM.202401004

收稿日期:2024-01-25 出版日期:2024-03-28 发布日期:2024-03-28

Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems

LU Zhizheng, HAN Yu(), JIN Shi()

National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China

Received:2024-01-25 Online:2024-03-28 Published:2024-03-28
About author:LU Zhizheng received his BS degree in communications engineering from Nanjing University of Science and Technology, China in 2021. Currently, he is pursuing his PhD degree in information and communications engineering from Southeast University, China. His research interests include extremely large-scale multiple-input multiple-output, near-field channel estimation, and hybrid reconfigurable intelligence surface.
HAN Yu (hanyu@seu.edu.cn) received her BS degree in communications engineering from Hangzhou Dianzi University, China in 2012, and MS and PhD degrees in information and communications engineering from Southeast University, China in 2015 and 2020, respectively. She was a postdoctoral fellow with Singapore University of Technology and Design, Singapore till 2022. Currently, she is an associate professor with Southeast University. Her research interests include extra large-scale MIMO and reconfigurable intelligent surface.
JIN Shi (jinshi@seu.edu.cn) received his BS degree in communications engineering from Guilin University of Electronic Technology, China in 1996, MS degree from Nanjing University of Posts and Telecommunications, China in 2003, and PhD degree in information and communications engineering from Southeast University, China in 2007. From June 2007 to October 2009, he was a research fellow with the Adastral Park Research Campus, University College London, UK. He is currently with the National Mobile Communications Research Laboratory, Southeast University. His research interests include wireless communications, random matrix theory, and information theory. He is serving as an area editor for the Transactions on Communications and IET Electronics Letters. He was an associate editor for the IEEE Transactions on Wireless Communications, IEEE Communications Letters, and IET Communications. Dr. JIN and his co-authors have been awarded the 2011 IEEE Communications Society Stephen O. Rice Prize Paper Award in the field of communication theory, the IEEE Vehicular Technology Society 2023 Jack Neubauer Memorial Award, a 2022 Best Paper Award and a 2010 Young Author Best Paper Award by the IEEE Signal Processing Society. He is an IEEE fellow.
Supported by:
the National Natural Science Foundation of China (NSFC)(62301148);the Natural Science Foundation of Jiangsu Province(BK20230824);the Key Technologies R&D Program of Jiangsu(BE2023022)

摘要/Abstract

Abstract:

Extremely large-scale hybrid reconfigurable intelligence surface (XL-HRIS), an improved version of the RIS, can receive the incident signal and enhance communication performance. However, as the RIS size increases, the phase variations of the received signal across the whole array are nonnegligible in the near-field region, and the channel model mismatch, which will decrease the estimation accuracy, must be considered. In this paper, the lower bound (LB) of the estimated parameter is studied and the impacts of the distance and signal-to-noise ratio (SNR) on LB are then evaluated. Moreover, the impacts of the array scale on LB and spectral efficiency (SE) are also studied. Simulation results verify that even in extremely large-scale array systems with infinite SNR, channel model mismatch can still limit estimation accuracy. However, this impact decreases with increasing distance.

Key words: XL-HRIS, near-field, LB, model mismatch, parameter estimation

. [J]. ZTE Communications, 2024, 22(1): 24-33.

LU Zhizheng, HAN Yu, JIN Shi. Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems[J]. ZTE Communications, 2024, 22(1): 24-33.

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参考文献 36

1	MATTHAIOU M, YURDUSEVEN O, NGO H Q, et al. The road to 6G: ten physical layer challenges for communications engineers [J]. IEEE communications magazine, 2021, 59(1): 64–69. DOI: 10.1109/MCOM.001.2000208
2	YANG H H, CAO X Y, YANG F, et al. A programmable metasurface with dynamic polarization, scattering and focusing control [J]. Scientific reports, 2016, 6: 35692. DOI: 10.1038/srep35692
3	ZHANG S W, ZHANG R. Capacity characterization for intelligent reflecting surface aided MIMO communication [J]. IEEE journal on selected areas in communications, 2020, 38(8): 1823–1838. DOI: 10.1109/JSAC.2020.3000814
4	DI RENZO M, ZAPPONE A, DEBBAH M, et al. Smart radio environments empowered by reconfigurable intelligent surfaces: how it works, state of research, and the road ahead [J]. IEEE journal on selected areas in communications, 2020, 38(11): 2450–2525. DOI: 10.1109/JSAC.2020.3007211
5	HUANG C, ZAPPONE A, ALEXANDROPOULOS G C, et al. Reconfigurable intelligent surfaces for energy efficiency in wireless communication [EB/OL]. (2018-10-06) [2019-01-10].
6	WU Q Q, ZHANG R. Intelligent reflecting surface enhanced wireless network via joint active and passive beamforming [J]. IEEE transactions on wireless communications, 2019, 18(11): 5394–5409. DOI: 10.1109/TWC.2019.2936025
7	HAN Y, TANG W K, JIN S, et al. Large intelligent surface-assisted wireless communication exploiting statistical CSI [J]. IEEE transactions on vehicular technology, 2019, 68(8): 8238–8242. DOI: 10.1109/TVT.2019.2923997
8	TANG W K, CHEN M Z, CHEN X Y, et al. Wireless communications with reconfigurable intelligent surface: path loss modeling and experimental measurement [J]. IEEE transactions on wireless communications, 2021, 20(1): 421–439. DOI: 10.1109/TWC.2020.3024887
9	CHEN W C, WEN C-K, LI X, et al. Channel customization for limited feedback in RIS-assisted FDD systems [J]. IEEE transactions on wireless communications, 2023, 22(7): 4505–4519. DOI: 10.1109/TWC.2022.3226442
10	MU X D, LIU Y W, GUO L, et al. Simultaneously transmitting and reflecting (STAR) RIS aided wireless communications [J]. IEEE transactions on wireless communications, 2022, 21(5): 3083–3098. DOI: 10.1109/TWC.2021.3118225
11	WEI L, HUANG C W, ALEXANDROPOULOS G C, et al. Channel estimation for RIS-empowered multi-user MISO wireless communications [J]. IEEE transactions on communications, 2021, 69(6): 4144–4157. DOI: 10.1109/TCOMM.2021.3063236
12	LIU C, LIU X M, NG D W K, et al. Deep residual learning for channel estimation in intelligent reflecting surface-assisted multi-user communications [J]. IEEE transactions on wireless communications, 2022, 21(2): 898–912. DOI: 10.1109/TWC.2021.3100148
13	WEI L, HUANG C W, GUO Q H, et al. Joint channel estimation and signal recovery for RIS-empowered multiuser communications [J]. IEEE transactions on communications, 2022, 70(7): 4640–4655. DOI: 10.1109/TCOMM.2022.3179771
14	JIN Y, ZHANG J Y, ZHANG X D, et al. Channel estimation for semi-passive reconfigurable intelligent surfaces with enhanced deep residual networks [J]. IEEE transactions on vehicular technology, 2021, 70(10): 11083–11088. DOI: 10.1109/TVT.2021.3109937
15	HU J F, YIN H F, BJÖRNSON E. MmWave MIMO communication with semi-passive RIS: a low-complexity channel estimation scheme [C]//Proc. IEEE Global Communications Conference (GLOBECOM). IEEE, 2021: 1–6. DOI: 10.1109/GLOBECOM46510.2021.9685434
16	LI M Y, ZHANG S, GE Y, et al. Joint channel estimation and data detection for hybrid RIS aided millimeter wave OTFS systems [J]. IEEE transactions on communications, 2022, 70(10): 6832–6848. DOI: 10.1109/TCOMM.2022.3199019
17	ALEXANDROPOULOS G C, VLACHOS E. A hardware architecture for reconfigurable intelligent surfaces with minimal active elements for explicit channel estimation [C]//Proc. 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020: 9175–9179. DOI: 10.1109/ICASSP40776.2020.9053976
18	ZHANG H Y, SHLEZINGER N, ALAMZADEH I, et al. Channel estimation with simultaneous reflecting and sensing reconfigurable intelligent metasurfaces [C]//Proc. IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). IEEE, 2021: 536–540. DOI: 10.1109/SPAWC51858.2021.9593172
19	SCHROEDER R, HE J G, BRANTE G, et al. Two-stage channel estimation for hybrid RIS assisted MIMO systems [J]. IEEE transactions on communications, 2022, 70(7): 4793–4806. DOI: 10.1109/TCOMM.2022.3176654
20	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
21	YANG S J, LYU W T, WANG D L, et al. Separate channel estimation with hybrid RIS-aided multi-user communications [J]. IEEE transactions on vehicular technology, 2023, 72(1): 1318–1324. DOI: 10.1109/TVT.2022.3205370
22	HEATH R W, GONZÁLEZ-PRELCIC N, RANGAN S, et al. An overview of signal processing techniques for millimeter wave MIMO systems [J]. IEEE journal of selected topics in signal processing, 2016, 10(3): 436–453. DOI: 10.1109/JSTSP.2016.2523924
23	FANG J, LI X J, LI H B, et al. Low-rank covariance-assisted downlink training and channel estimation for FDD massive MIMO systems [J]. IEEE transactions on wireless communications, 2017, 16(3): 1935–1947. DOI: 10.1109/TWC.2017.2657513
24	HAN Y, HSU T H, WEN C K, et al. Efficient downlink channel reconstruction for FDD transmission systems [C]//Proc. 27th Wireless and Optical Communication Conference (WOCC). IEEE, 2018: 1–5. DOI: 10.1109/WOCC.2018.8372727
25	HAN Y, HSU T H, WEN C K, et al. Efficient downlink channel reconstruction for FDD multi-antenna systems [J]. IEEE transactions on wireless communications, 2019, 18(6): 3161–3176. DOI: 10.1109/TWC.2019.2911497
26	HAN Y, LIU Q, WEN C-K, et al. Tracking FDD massive MIMO downlink channels by exploiting delay and angular reciprocity [J]. IEEE journal of selected topics in signal processing, 2019, 13(5): 1062–1076. DOI: 10.1109/JSTSP.2019.2935320
27	SELVAN K T, JANASWAMY R. Fraunhofer and Fresnel distances: unified derivation for aperture antennas [J]. IEEE antennas and propagation magazine, 2017, 59(4): 12–15. DOI: 10.1109/MAP.2017.2706648
28	SHI X, WANG J, SUN Z, et al. Spatial-chirp codebook-based hierarchical beam training for extremely large-scale massive MIMO [EB/OL]. (2022-10-07) [2023-08-15].
29	SOUTHWELL W H. Validity of the Fresnel approximation in the near field [J]. Journal of the optical society of America, 1981, 71(1): 7. DOI: 10.1364/josa.71.000007
30	CUI M Y, DAI L L. Channel estimation for extremely large-scale MIMO: far-field or near-field? [J]. IEEE transactions on communications, 2022, 70(4): 2663–2677. DOI: 10.1109/TCOMM.2022.3146400
31	YANG J, ZENG Y, JIN S, et al. Communication and localization with extremely large lens antenna array [J]. IEEE transactions on wireless communications, 2021, 20(5): 3031–3048. DOI: 10.1109/TWC.2020.3046766
32	LU Z Z, HAN Y, JIN S, et al. Near-field channel reconstruction and user localization for ELAA systems [C]//Proc. International Symposium on Wireless Communication Systems (ISWCS). IEEE, 2022: 1–6. DOI: 10.1109/ISWCS56560.2022.9940362
33	MAMANDIPOOR B, RAMASAMY D, MADHOW U. Newtonized orthogonal matching pursuit: frequency estimation over the continuum [J]. IEEE transactions on signal processing, 2016, 64(19): 5066–5081. DOI: 10.1109/TSP.2016.2580523
34	OZTURK C, KESKIN M F, WYMEERSCH H, et al. RIS-aided near-field localization under phase-dependent amplitude variations [J]. IEEE transactions on wireless communications, 2023, 22(8): 5550–5566. DOI: 10.1109/TWC.2023.3235306
35	FENG K M, LI X, HAN Y, et al. Joint beamforming optimization for reconfigurable intelligent surface-enabled MISO-OFDM systems [J]. China communications, 2021, 18(3): 63–79. DOI: 10.23919/JCC.2021.03.006
36	LU Z, HAN Y, JIN S, et al. Near-filed localization and channel estimation for ELAA systems [J]. IEEE transactions on wireless communications, 2023, Early Access. DOI: 10.1109/TWC.2023.3336328

Impacts of Model Mismatch and Array Scale on Channel Estimation for XL-HRIS-Aided Systems

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