Bayesian Blind Detection Algorithm Based on Multi-User Serial Interference Cancellation

被引:0
|
作者
Wu Q. [1 ]
Si Z. [1 ]
Dai J. [1 ]
Wang S. [2 ]
Yuan Y. [2 ]
机构
[1] The Key Laboratory of Universal Wireless Communications(Ministry of Education), Beijing University of Posts and Telecommunications, Beijing
[2] China Mobile Research Institute, Beijing
关键词
Bayesian inference; message passing algorithm; multiuser detection; serial interference cancellation;
D O I
10.13190/j.jbupt.2022-261
中图分类号
学科分类号
摘要
In the massive machine type communication, user devices are allowed to randomly access the network and transmit small packets occasionally by grant-free transmission. Correspondingly, receivers are required to perform the blind multi-user detection without scheduling and pilots. The Bayesian blind detection algorithm based on message passing can solve the above problem, but the parallel iterative calculation consumes massive computing resources with high computational complexity and unstable convergence. An algorithm combining serial interference cancellation with Bayesian message passing is proposed to improve the performance of the blind multi-user detection. By iteratively reconstructing and canceling the interference of correctly recovered users, the signal to interference plus noise ratio at the receiver is improved, which enhances the error performance and reduces the computational complexity. Meanwhile, the convergence stability is promoted by damping and re-initialization mechanisms. Simulation results show that the proposed algorithm has obvious advantages over the parallel Bayesian blind detection algorithm in the blind multiuser detection. © 2024 Beijing University of Posts and Telecommunications. All rights reserved.
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页码:1 / 6and37
页数:636
相关论文
共 10 条
  • [1] PIRINEN P., A brief overview of 5G research activities [C]椅1st International Conference on 5G for Ubiquitous Connectivity, pp. 17-22, (2014)
  • [2] TULLBERG H, POPOVSKI P, LI Z, Et al., The METIS 5G system concept: meeting the 5G requirements [J], IEEE Communications Magazine, 54, 12, pp. 132-139, (2016)
  • [3] ZHANG L, XIAO M, WU G, Et al., A survey of advanced techniques for spectrum sharing in 5G networks, IEEE Wireless Communications, 24, 5, pp. 44-51, (2017)
  • [4] LI P, PAUL D, NARASIMHAN R, Et al., On the distribution of SINR for the MMSE MIMO receiver and performance analysis, IEEE Transactions on Information Theory, 52, 1, pp. 271-286, (2006)
  • [5] JI H, PARK S, SHIM B., Sparse vector coding for ultra reliable and low latency communications, IEEE Transactions on Wireless Communications, 17, 10, pp. 6693-6706, (2018)
  • [6] ABEBE A T, KANG C G., Comprehensive grant-free random access for massive & low latency communication, IEEE International Conference on Communications (ICC), pp. 1-6, (2017)
  • [7] JIANG S C, YUAN X J, WANG X, Et al., Joint user identification, channel estimation, and signal detection for grant-free NOMA, IEEE Transactions on Wireless Communications, 19, 10, pp. 6960-6976, (2020)
  • [8] ZHANG Y Y, YUAN Z D, GUO Q H, Et al., Bayesian receiver design for grant-free NOMA with message passing based structured signal estimation, IEEE Transactions on Vehicular Technology, 69, 8, pp. 8643-8656, (2020)
  • [9] TAKEDA T, HIGUCHI K., Enhanced user fairness using non-orthogonal access with SIC in cellular uplink, 椅2011 IEEE Vehicular Technology Conference (VTC Fall), pp. 1-5, (2011)
  • [10] SCHNITER P, RANGAN S., Compressive phase retrieval via generalized approximate message passing, IEEE Transactions on Signal Processing, 63, 4, pp. 1043-1055, (2014)