ISSN 0253-2778

CN 34-1054/N

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Open AccessOpen Access JUSTC Original Paper

Active user detection and channel estimation based on expectation propagation

  • Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 23026, China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2019.10.004
  • Received Date: 11 April 2019
  • Accepted Date: 28 May 2019
  • Rev Recd Date: 28 May 2019
  • Publish Date: 31 October 2019
    Abstract

  • Abstract

    In the 5th-generation (5G) wireless communication network, massive machine type communication (mMTC) is an emerging research topic. For mMTC, non-orthogonal multiple access (NOMA) has been proposed to support its large-scale connectivity. Due to the sparsity of mMTC, compressed sensing based algorithms can be used to identify the active users and recover the sparse channel state information (CSI) vector. A Bayesian message passing algorithm based on expectation propagation (EP) is proposed for joint active user detection (AUD) and channel estimation (CE) in NOMA. The proposed method approximates the complicated target distribution with a Gaussian distribution to achieve linear complexity. By introducing a damping factor, the convergence performance of the algorithm can be effectively ensured. Simulations demonstrate that the EP-based algorithm can achieve better performance in joint AUD and CE than the exiting algorithms, especially in the low SNR regime.

    Abstract

    In the 5th-generation (5G) wireless communication network, massive machine type communication (mMTC) is an emerging research topic. For mMTC, non-orthogonal multiple access (NOMA) has been proposed to support its large-scale connectivity. Due to the sparsity of mMTC, compressed sensing based algorithms can be used to identify the active users and recover the sparse channel state information (CSI) vector. A Bayesian message passing algorithm based on expectation propagation (EP) is proposed for joint active user detection (AUD) and channel estimation (CE) in NOMA. The proposed method approximates the complicated target distribution with a Gaussian distribution to achieve linear complexity. By introducing a damping factor, the convergence performance of the algorithm can be effectively ensured. Simulations demonstrate that the EP-based algorithm can achieve better performance in joint AUD and CE than the exiting algorithms, especially in the low SNR regime.
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    • References(18)
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    GOMAA A, AL-DHAHIR N. A sparsity-aware approach for NBI estimation in MIMO-OFDM[J]. IEEE Transactions on Wireless Communications, 2011, 10(6): 1854-1862.
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    ZOU Q, ZHANG H, WEN C K, et al. Concise derivation for generalized approximate message passing using expectation propagation[J]. IEEE Signal Processing Letters, 2018, 25(12): 1835-1839.
    [13]
    MINKA T P. Expectation propagation for approximate Bayesian inference[C]//Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. Pittsburgh: Morgan Kaufmann Publishers Inc., 2001: 362-369.
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    RASMUSSEN CE, WILLIAMS K I. Gaussian Process for Machine Learning[M]. The MIT Press, 2006.
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    VILA J, SCHNITER P, RANGAN S, et al. Adaptive damping and mean removal for the generalized approximate message passing algorithm[C]//2015 IEEE International Conference on Acoustics, Speech and Signal Processing. Brisbane, Australia: IEEE, 2015: 2021-2025.
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    CALTAGIRONE F, ZDEBOROV L, KRZAKALA F. On convergence of approximate message passing[C]//2014 IEEE International Symposium on Information Theory. Honolulu, USA:IEEE, 2014: 1812-1816.
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    SCHNITER P, RANGAN S. Compressive phase retrieval via generalized approximate message passing[J]. IEEE Transactions on Signal Processing, 2015, 63(4): 1043-1055.
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    [1]
    TULLBERG H, POPOVSKI P, LI Z, et al. The METIS 5G system concept: Meeting the 5G requirements[J]. IEEE Communications Magazine, 2016, 54(12): 132-139.
    [2]
    DING Z, DAI L, POOR H V. MIMO-NOMA design for small packet transmission in the Internet of things[J]. IEEE Access, 2016, 4: 1393-1405.
    [3]
    LIU Y, QIN Z, ELKASHLAN M, et al. Non-orthogonal multiple access for 5G and beyond[J]. arXiv Preprint 2018, arXiv:1808.00277.
    [4]
    HONG J P, CHOI W, RAO B D. Sparsity controlled random multiple access with compressed sensing[J]. IEEE Transactions on Wireless Communications, 2015, 14(2): 998-1010.
    [5]
    DONOHO D L. Compressed sensing[J]. IEEE Transactions on information theory, 2006, 52(4): 1289-1306.
    [6]
    ZHU H, GIANNAKIS G B. Exploiting sparse user activity in multiuser detection[J]. IEEE Transactions on Communications, 2011, 59(2): 454-465.
    [7]
    KNOOP B, MONSEES F, BOCKELMANN C, et al. Sparsity-aware successive interference cancellation with practical constraints[C]// 17th International ITG Workshop on Smart Antennas. StuTTGART, GERMANY:VDE, 2013: 1-8.
    [8]
    GOMAA A, AL-DHAHIR N. A sparsity-aware approach for NBI estimation in MIMO-OFDM[J]. IEEE Transactions on Wireless Communications, 2011, 10(6): 1854-1862.
    [9]
    KNOOP B, SCHMALE S, PETERS-DROLSHAGEN D, et al. Activity and channel estimation in multi-user wireless sensor networks[C]// 20th International ITG Workshop on Smart Antennas. Munich, Germany: VDE, 2016: 1-5.
    [10]
    HANNAK G, MAYER M, JUNG A, et al. Joint channel estimation and activity detection for multiuser communication systems[C]//2015 IEEE International Conference on Communication Workshop. London: IEEE, 2015: 2086-2091.
    [11]
    RANGAN S. Generalized approximate message passing for estimation with random linear mixing[C]//2011 IEEE International Symposium on Information Theory Proceedings. IEEE, 2011: 2168-2172.
    [12]
    ZOU Q, ZHANG H, WEN C K, et al. Concise derivation for generalized approximate message passing using expectation propagation[J]. IEEE Signal Processing Letters, 2018, 25(12): 1835-1839.
    [13]
    MINKA T P. Expectation propagation for approximate Bayesian inference[C]//Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence. Pittsburgh: Morgan Kaufmann Publishers Inc., 2001: 362-369.
    [14]
    RASMUSSEN CE, WILLIAMS K I. Gaussian Process for Machine Learning[M]. The MIT Press, 2006.
    [15]
    VILA J, SCHNITER P, RANGAN S, et al. Adaptive damping and mean removal for the generalized approximate message passing algorithm[C]//2015 IEEE International Conference on Acoustics, Speech and Signal Processing. Brisbane, Australia: IEEE, 2015: 2021-2025.
    [16]
    CALTAGIRONE F, ZDEBOROV L, KRZAKALA F. On convergence of approximate message passing[C]//2014 IEEE International Symposium on Information Theory. Honolulu, USA:IEEE, 2014: 1812-1816.
    [17]
    SCHNITER P, RANGAN S. Compressive phase retrieval via generalized approximate message passing[J]. IEEE Transactions on Signal Processing, 2015, 63(4): 1043-1055.
    [18]
    BEYME S, LEUNG C. Efficient computation of DFT of Zadoff-Chu sequences[J]. Electronics letters, 2009, 45(9): 461-463.
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