Channel estimation for massive MIMO system using the shannon entropy function

被引:2
作者
Albataineh, Zaid [1 ]
Al-Zoubi, Nebal [2 ]
Musa, Ahmed [2 ]
机构
[1] Yarmouk Univ, Dept Elect Engn, Irbid 21163, Jordan
[2] Yarmouk Univ, Dept Commun Engn, Irbid 21163, Jordan
来源
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS | 2023年 / 26卷 / 06期
关键词
Wireless networks and communication; Massive MIMO; Shannon entropy function (SEF); Channel estimation; Compressive sensing (CS); Normalized mean square error (NMSE); Frequency division duplexing (FDD); SIGNAL RECOVERY; ENERGY-EFFICIENT; WIRELESS;
D O I
10.1007/s10586-022-03783-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Massive MIMO systems with a large number of antennas at the base station (BS) may significantly boost spectrum and energy efficiency. In massive MIMO systems, it's important to have accurate channel state information (CSI) to get the most out of the large number of antennas and make sure the system works well. But because there are so many antennas at the base station (BS), the massive MIMO system has a lot of pilot overhead, which hurts system performance a lot. The spatial correlations between the signal sources in MIMO systems are low. This pattern of distribution makes it possible to use compressive sensing in massive MIMO systems to solve the channel estimation problem. In this study, we used the Shannon entropy function to come up with a new way to estimate the channel in the downlink of an FDD massive MIMO system. The Shannon entropy function is used as a sparsity regularizer for downlink channel estimation in the presented method to reduce the amount of work done by the pilot. The simulation results show that the proposed system outperforms existing compressive sensing (CS)-based channel estimation techniques in terms of NMSE performance and effectively lowers pilot overhead.
引用
收藏
页码:3793 / 3801
页数:9
相关论文
共 41 条
  • [1] Efficient user-channel pairing with power-domain sum-rate maximization in opportunistic hybrid OFDMA-NOMA IoT systems
    Abdel-Razeq, Sharief
    Al-Obiedollah, Haitham
    Bany Salameh, Haythem
    [J]. CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2022, 25 (04): : 2501 - 2514
  • [2] Sparse Signal Representation, Sampling, and Recovery in Compressive Sensing Frameworks
    Ahmed, Irfan
    Khalil, Amaad
    Ahmed, Ishtiaque
    Frnda, Jaroslav
    [J]. IEEE ACCESS, 2022, 10 : 85002 - 85018
  • [3] OPTIMAL PILOT SEQUENCE DESIGN FOR MACHINE LEARNING BASED CHANNEL ESTIMATION IN FDD MASSIVE MIMO SYSTEMS
    AL-Salihi, Hayder
    Al-Gharbawi, Mohammed
    Said, Fatin
    [J]. 2021 ITU KALEIDOSCOPE CONFERENCE: CONNECTING PHYSICAL AND VIRTUAL WORLDS (ITU K), 2021, : 23 - 30
  • [4] Energy-Efficient beyond 5G Multiple Access Technique with Simultaneous Wireless Information and Power Transfer for the Factory of the Future
    Albataineh, Zaid
    Andrawes, Admoon
    Abdullah, Nor Fadzilah
    Nordin, Rosdiadee
    [J]. ENERGIES, 2022, 15 (16)
  • [5] Low-Complexity Near-Optimal Iterative Signal Detection Based on MSD-CG Method for Uplink Massive MIMO Systems
    Albataineh, Zaid
    [J]. WIRELESS PERSONAL COMMUNICATIONS, 2021, 116 (03) : 2549 - 2563
  • [6] Two pairwise iterative schemes for high dimensional blind source separation
    Albataineh, Zaid
    Salem, Fathi
    [J]. INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, 2021, 24 (04) : 957 - 968
  • [7] Robust massive MIMO channel estimation for 5G networks using compressive sensing technique
    Albataineh, Zaid
    Hayajneh, Khaled
    Salameh, Haythem Bany
    Dang, Chinh
    Dagmseh, Ahmad
    [J]. AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS, 2020, 120
  • [8] Blind Decoding of Massive MIMO Uplink Systems Based on the Higher Order Cumulants
    Albataineh, Zaid
    [J]. WIRELESS PERSONAL COMMUNICATIONS, 2018, 103 (02) : 1835 - 1847
  • [9] Albataineh Z, 2013, IEEE INT SYMP CIRC S, P1946, DOI 10.1109/ISCAS.2013.6572249
  • [10] Deep Learning for Massive MIMO Uplink Detectors
    Albreem, Mahmoud A.
    Alhabbash, Alaa H.
    Shahabuddin, Shahriar
    Juntti, Markku
    [J]. IEEE COMMUNICATIONS SURVEYS AND TUTORIALS, 2022, 24 (01): : 741 - 766