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Channel feedback based on complex 1-bit Bayesian compressed sensing in FDD massive MIMO systems

Published: 19 December 2019 Publication History

Abstract

In frequency division duplex (FDD) massive multi-input multi-output (MIMO) system, channel feedback is critical for beamforming and precoding. The previous codebook-based feedback scheme in 4G system cannot be reused since the codebook size will scale exponentially with the number of antennas. In the paper, we propose a 1-bit compressed sensing (CS) based channel feedback scheme. The sparse massive MIMO channel is estimated at the user equipment (UE), then it is compressed by 1-bit quantization at the UE. The quantized bits are fed back to the base station (BS) and are recovered by the complex 1-bit Bayesian CS algorithm which can also be used for other complex 1-bit CS models. The recovered channel is used for zero forcing beamforming for multiple user (MU) massive MIMO. The performance of the proposed algorithm is compared with other feedback schemes in view of per-user capacity in MU massive MIMO.

References

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K Kim, T Kim, D J Love and I H Kim (2012). Differential feedback in codebook-based multiuser MIMO systems in slowly varying channels. IEEE Transactions on Communications, 60(2), 578--588.
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B Lee, J Choi, J Y Seol, D J Love and B Shim (2015). Antenna grouping based feedback compression for FDD-based massive MIMO systems. IEEE Transactions on Communications, 63(9), 3261--3274.
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D Sacristán-Murga and A Pascual-Iserte (2010). Differential feedback of MIMO channel gram matrices based on geodesic curves. IEEE Transactions on Wireless Communications, 9(12), 2553--2556.
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W Shen, L Dai, G Gui, Z Wang, R W Heath and F Adachi (2017). AoD-adaptive subspace codebook for channel feedback in FDD massive MIMO systems, in 2017 IEEE International Conference on Communications (ICC), Paris, 2017, pp. 1--5.
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W Shen, L Dai, Y Shi, B Shim and Z Wang (2015). Joint channel training and feedback for FDD massive MIMO systems. IEEE Transactions on Vehicular Technology, 65(10), 8762--8767.
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W Lu, Y Wang, X Wen, X Hua, S Peng and L Zhong (2019). Compressive downlink channel estimation for FDD massive MIMO using weighted lp minimization. IEEE Access.
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V K N Lau, S Cai and A Liu (2016). Closed-loop compressive CSIT estimation in FDD massive MIMO systems with 1 bit feedback. IEEE Transactions on Signal Processing, 64(8), 2146--2155.
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S Jacobsson, G Durisi, M Coldrey, T Goldstein and C Studer (2016). Nonlinear 1-bit precoding for massive MU-MIMO with higher-order modulation, in 2016 50th Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, 2016, pp. 763--767.
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3GPP TR25.996 (2018). Spatial channel model for multiple input multiple output (MIMO) simulations (Version 15.0.0, 2018.06), https://standards.globalspec.com/std/756581/3GPP TR 25.996.
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W Lu, Y Wang, X Wen, S Peng and L Zhong (2019). Downlink channel estimation in massive multiple-input multiple-output with correlated sparsity by overcomplete dictionary and bayesian inference. Electronics, 8, 473, 1--16.
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M S Sim, J Park, C B Chae and R W Heath (2015). Compressed channel feedback for correlated massive MIMO systems. Journal of Communications and Networks, 18(1), 95--104.

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  1. Channel feedback based on complex 1-bit Bayesian compressed sensing in FDD massive MIMO systems

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    AIIPCC '19: Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing
    December 2019
    464 pages
    ISBN:9781450376334
    DOI:10.1145/3371425
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 19 December 2019

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    Author Tags

    1. 1-bit compressed sensing
    2. channel feedback
    3. massive MIMO

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    • Research-article

    Funding Sources

    • National Science Foundation of China
    • China Postdoctoral Science Foundation Grant
    • the self-determined research funds of CCNU

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    AIIPCC '19
    Sponsor:
    • ASciE

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    AIIPCC '19 Paper Acceptance Rate 78 of 211 submissions, 37%;
    Overall Acceptance Rate 78 of 211 submissions, 37%

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