Approximate Message Passing-Based Joint User Activity and Data Detection for NOMA

被引:107
|
作者
Wei, Chao [1 ]
Liu, Huaping [2 ]
Zhang, Zaichen [1 ]
Dang, Jian [1 ]
Wu, Liang [1 ]
机构
[1] Southeast Univ, Natl Mobile Commun Res Lab, Nanjing 210096, Jiangsu, Peoples R China
[2] Oregon State Univ, Sch Elect Engn & Comp Sci, Corvallis, OR 97331 USA
关键词
5G; grant-free non-orthogonal multiple access (NOMA); multiuser detection (MUD); approximate message passing (AMP); expectation maximization (EM); ALGORITHM;
D O I
10.1109/LCOMM.2016.2624297
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
This letter focuses on joint user activity and data detection in the uplink grant-free non-orthogonal multiple access systems based on approximate message passing (AMP) and expectation maximization (EM) algorithms. The proposed Joint-EM-AMP detection algorithm consists of three steps in each iteration. First, AMP decouples the superimposed received signal into uncoupled scalar problems. Then, at the denoising step, AMP computes the posterior means and variances of the transmitted symbols with the extended modulation constellation. The third step is to estimate user activity parameters using EM based on the frame-wise joint sparsity of user activity. In contrast to existing state-of-the-art algorithms, the proposed Joint-EM-AMP algorithm demonstrates significant performance gain in terms of bit error rate, which will be verified in simulation results.
引用
收藏
页码:640 / 643
页数:4
相关论文
共 50 条
  • [41] Expectation-maximization vector approximate message passing-based frequency-domain turbo equalization for underwater acoustic communications
    Zhang, Xinrui
    Tao, Jun
    Li, Dong
    Wu, Yanbo
    Chen, Wenxuan
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 2023, 154 (05): : 3344 - 3353
  • [42] Speech Enhancement Based on Approximate Message Passing
    Chao Li
    Ting Jiang
    Sheng Wu
    中国通信, 2020, 17 (08) : 187 - 198
  • [43] Speech Enhancement Based on Approximate Message Passing
    Li, Chao
    Jiang, Ting
    Wu, Sheng
    CHINA COMMUNICATIONS, 2020, 17 (08) : 187 - 198
  • [44] Joint User Activity and Data Detection in Grant-Free NOMA using Generative Neural Networks
    Zou, Yixuan
    Qin, Zhijin
    Liu, Yuanwei
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [45] Joint User Activity and Data Detection for NOMA via the Integrated Framework of Expectation Maximization and Expectation Propagation
    Zhang, Lei
    Meng, Xiangming
    Wang, Lei
    Chen, Yan
    2019 IEEE 20TH INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING ADVANCES IN WIRELESS COMMUNICATIONS (SPAWC 2019), 2019,
  • [46] Gaussian message passing-based cooperative localization on factor graph in wireless networks
    Li, Bin
    Wu, Nan
    Wang, Hua
    Tseng, Po-Hsuan
    Kuang, Jingming
    SIGNAL PROCESSING, 2015, 111 : 1 - 12
  • [47] A LSE and Sparse Message Passing-Based Channel Estimation for mmWave MIMO Systems
    Huang, Chongwen
    Liu, Lei
    Yuen, Chau
    Sun, Sumei
    2016 IEEE GLOBECOM WORKSHOPS (GC WKSHPS), 2016,
  • [48] Deep Learning Based Trainable Approximate Message Passing for Massive MIMO Detection
    Zheng, Peicong
    Zeng, Yuan
    Liu, Zhenrong
    Gong, Yi
    ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2020,
  • [49] Message Passing-Based Link Configuration in Short Range Millimeter Wave Systems
    Myers, Nitin Jonathan
    Kaleva, Jarkko
    Tolli, Antti
    Heath, Robert W., Jr.
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2020, 68 (06) : 3465 - 3479
  • [50] Approximate Message Passing-Enhanced Graph Neural Network for OTFS Data Detection
    Zhuang, Wenhao
    Mao, Yuyi
    He, Hengtao
    Xie, Lei
    Song, Shenghui
    Ge, Yao
    Ding, Zhi
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2024, 13 (07) : 1913 - 1917