可重构智能面辅助的低精度量化大规模MIMO系统的信道估计

doi:10.12305/j.issn.1001-506X.2021.10.34

Abstract

Abstract:

The problem of channel estimation for reconfigurable intelligent surface assisted massive multiple input multiple output (MIMO) systems in view of low-resolution quantization is focused on. The main challenge for channel estimation of this system lies in that the reconfigurable intelligent surface(RIS) is composed of reconfigurable and nearly passive reflecting antennas which has no signal processing capability. Meanwhile, considering the situation that the observations is quantized by the low-resolution analog-to-digital converters, the channel estimation problem for such system becomes more challenging. By introducing the effective channel model derived from the cascaded channel model among the base station, the RIS and the user equipment, and proving that the effective channel can be considered as a structured sparse signal in the virtual angular domain. An expectation-maximization-based nearest neighbor learning generalized approximate message passing algorithm is proposed to recover the effective channel from the low-resolution quantized observations. Simulation results illustrate that the proposed algorithm can obtain better performance than conventional algorithms.

Key words: massive multiple input multiple output system, compressed sensing, reconfigurable intelligent surface, effective channel estimation

CLC Number:

TN918

Binrui LI, Zhongpei ZHANG. Channel estimation for reconfigurable intelligent surface assisted low-resolution quantized massive MIMO[J]. Systems Engineering and Electronics, 2021, 43(10): 2984-2991.

Figures/Tables 7

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References 30

1	MARZETTA T L , NGO H Q . Fundamentals of massive MIMO[M]. Cambridge: Cambridge University Press, 2016.
2	BASAR E , DI RENZO M , DE R J , et al. Wireless communications through reconfigurable intelligent surfaces[J]. IEEE Access, 2019, 7, 116753- 116773. doi: 10.1109/ACCESS.2019.2935192
3	DAI L L , WANG B C , WANG M , et al. Reconfigurable intelligent surface-based wireless communications: antenna design, prototyping, and experimental results[J]. IEEE Access, 2020, 8, 45913- 45923. doi: 10.1109/ACCESS.2020.2977772
4	TANG W , CHEN M Z , DAI J Y , et al. Wireless communications with programmable meta surface: new paradigms, opportunities, and challenges on transceiver design[J]. IEEE Wireless Communications, 2020, 27 (2): 180- 187. doi: 10.1109/MWC.001.1900308
5	TANG W , LI X , DAI J Y , et al. Wireless communications with programmable met surface: transceiver design and experimental results[J]. China Communications, 2019, 16 (5): 46- 61. doi: 10.23919/j.cc.2019.05.004
6	NGUYEN S L H, GHRAYEB A. Compressive sensing-based channel estimation for massive multiuser MIMO systems[C]//Proc. of the IEEE Wireless Communications and Networking Conference, 2013: 2890-2895.
7	QI C , WU L . Uplink channel estimation for massive MIMO systems exploring joint channel sparsity[J]. Electronics Letters, 2014, 50 (23): 1770- 1772. doi: 10.1049/el.2014.2769
8	RAO X , LAU V K N . Distributed compressive CSIT estimation and feedback for FDD multi-user massive MIMO systems[J]. IEEE Trans.on Signal Processing, 2014, 62 (12): 3261- 3271. doi: 10.1109/TSP.2014.2324991
9	GAO Z , DAI L L , WANG Z , et al. Spatially common sparsity based adaptive channel estimation and feedback for FDD massive MIMO[J]. IEEE Trans.on Signal Processing, 2015, 63 (23): 6169- 6183. doi: 10.1109/TSP.2015.2463260
10	XIU Y , WANG W Y , ZHANG Z P . A message passing approach to acquire mmwave channel state information based on out-of-band data[J]. IEEE Access, 2018, 6, 45665- 45680. doi: 10.1109/ACCESS.2018.2855688
11	NADEEM Q U A , ALWAZANI H , KAMMOUN A , et al. Intelligent reflecting surface assisted multi-user MISO communication: channel estimation and beamforming design[J]. IEEE Open Journal of the Communications Society, 2020, 1, 661- 680. doi: 10.1109/OJCOMS.2020.2992791
12	JENSEN T L, CARVALHOD E. An optimal channel estimation scheme for intelligent reflecting surfaces based on a minimum variance unbiased estimator[C]//Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2020: 5000-5004.
13	HE Z Q , YUAN X . 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
14	HU C, DAI L L. Two-timescale channel estimation for reconfigurable intelligent surface aided wireless communications[EB/OL]. [2021-09-26]. https://ieeexplore.ieee.org/document/9400843.
15	CHEN J, LIANG Y C, CHENG H V, et al. Channel estimation for reconfigurable intelligent surface aided multi-user MIMO systems[EB/OL]. [2021-03-26]. https://arxiv.org/pdf/1912.03619.pdf.
16	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
17	WEI L, HUANG C C, ALEXANDROPOULOS G C, et al. Parallel factor decomposition channel estimation in RIS-assisted multi-user MISO communication[C]//Proc. of the IEEE 11th Sensor Array and Multichannel Signal Processing Workshop, 2020.
18	WEI X H , SHEN D C , DAI L L . Channel estimation for RIS assisted wireless communications: part Ⅱ—an improved solution based on double-structured sparsity[J]. IEEE Communications Letters, 2021, 25 (5): 1403- 1407. doi: 10.1109/LCOMM.2021.3052787
19	WEI X H , SHEN D C , DAI L L . Channel estimation for RIS assisted wireless communications: part I—fundamentals, solutions, and future opportunities[J]. IEEE Communications Letters, 2021, 25 (5): 1398- 1402. doi: 10.1109/LCOMM.2021.3052822
20	XIONG Y Z , ZHANG Z P , WEI N , et al. Performance analysis of uplink massive MIMO systems with variable-resolution ADCs using MMSE and MRC detection[J]. Transactions on Emerging Telecommunications Technologies, 2019, 30 (5): e3549. doi: 10.1002/ett.3549
21	ZHANG J Y , DAI L L , SUN S Y , et al. On the spectral efficiency of massive MIMO systems with low-resolution ADCs[J]. IEEE Communications Letters, 2016, 20 (5): 842- 845. doi: 10.1109/LCOMM.2016.2535132
22	WANG S C , LI Y Z , WANG J . Multiuser detection in massive spatial modulation MIMO with low-resolution ADCs[J]. IEEE Trans.on Wireless Communications, 2014, 14 (4): 2156- 2168.
23	JACQUES L, DEGRAUX K, DE V C. Quantized iterative hard thresholding: bridging 1-bit and high-resolution quantized compressed sensing[EB/OL]. [2021-03-26]. http://arxiv.org/pdf/1305.1786.pdf
24	ZYMNIS A , BOYD S , CANDES E . Compressed sensing with quantized measurements[J]. IEEE Signal Processing Letters, 2009, 17 (2): 149- 152.
25	XIONG Y Z , ZHANG Z P , WEI N , et al. A bilinear GAMP-based receiver for quantized mmwave massive MIMO using expectation maximization[J]. IEEE Communications Letters, 2018, 23 (1): 84- 87.
26	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 Trans.on Signal Processing, 2015, 64 (10): 2541- 2556.
27	MO J , SCHNITER P , HEATH R W . Channel estimation in broadband millimeter wave MIMO systems with few-bit ADCs[J]. IEEE Trans.on Signal Processing, 2017, 66 (5): 1141- 1154.
28	ZHI K D , PAN C , REN H , et al. Uplink achievable rate of intelligent reflecting surface-aided millimeter-wave communications with low-resolution ADC and phase noise[J]. IEEE Wireless Communications Letters, 2020,
29	GRAY R M , NEUHOFF D L . Quantization[J]. IEEE Trans.on Information Theory, 1998, 44 (6): 2325- 2383. doi: 10.1109/18.720541
30	LIN X C , WU S , JIANG C X , et al. Estimation of broadband multiuser millimeter wave massive MIMO-OFDM channels by exploiting their sparse structure[J]. IEEE Trans.on Wireless Communications, 2018, 17 (6): 3959- 3973. doi: 10.1109/TWC.2018.2818142

Q bit	Δ_norm
1	1.595 8
2	0.995 7
3	0.586 0
4	0.335 2
5	0.188 1

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Channel estimation for reconfigurable intelligent surface assisted low-resolution quantized massive MIMO

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