Low complexity IAOR signal detection for uplink GSM - massive MIMO systems

被引:0
|
作者
Hanchate, Seema M. [1 ]
Nema, Shikha [1 ]
机构
[1] SNDT Womens Univ, Usha Mittal Inst Technol, Mumbai, Maharashtra, India
关键词
massive MIMO; MMSE signal detection; accelerated over-relaxation; channel correlation; computational complexity; uplink transmission; MODULATION; NETWORK;
D O I
10.1504/IJCNDS.2024.141674
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 multiple input multiple output (Mamimo) is a high-speed wireless communication that uses more antennas at the receiver base station. When more antennas are utilised, the channel matrix gets larger. As a result of the inversion channel matrix, the complexity of linear signal detection grows. Mamimo consumes more power due to radio-frequency (RF) chains. The channel magnitude and correlation are used to implement the antenna selection process. The antenna selection method reduces the number of RF chains and improves BER performance. To avoid expensive matrix inversion, the improved accelerated over relaxation (IAOR) signal detection algorithm is presented. For various antenna designs, the bit error rate is calculated. According to the result of MATLAB simulation, the performance of the standard minimum mean square error (MMSE) detection and the proposed method are approximately equal. The proposed IAOR signal detection method improves the overall performance and energy efficiency and also reduces computational complexity.
引用
收藏
页码:699 / 710
页数:13
相关论文
共 50 条
  • [21] A Low Complexity Signal Detection Scheme Based on Improved Newton Iteration for Massive MIMO Systems
    Jin, Fangli
    Liu, Qiufeng
    Liu, Hao
    Wu, Peng
    IEEE COMMUNICATIONS LETTERS, 2019, 23 (04) : 748 - 751
  • [22] Low-Complexity Symbol Detection for Index Modulated Massive MIMO Systems
    Mandloi, Manish
    Sharma, Sanjeev
    Pattanayak, Prabina
    Gurjar, Devendra S.
    2020 ADVANCED COMMUNICATION TECHNOLOGIES AND SIGNAL PROCESSING (IEEE ACTS), 2020,
  • [23] A Distributed Detection Algorithm For Uplink Massive MIMO Systems
    Liu, Qiufeng
    Liu, Hao
    Yan, Ying
    Wu, Peng
    PROCEEDINGS OF THE 2019 IEEE INTERNATIONAL WORKSHOP ON SIGNAL PROCESSING SYSTEMS (SIPS 2019), 2019, : 213 - 217
  • [24] Low-Complexity Multiuser QAM Detection for Uplink 1-bit Massive MIMO
    Alevizos, Panos N.
    IEEE COMMUNICATIONS LETTERS, 2018, 22 (08) : 1592 - 1595
  • [25] Iterative Signal Detection Based on MSD-CG Method for Uplink Massive MIMO Systems
    Albataineh, Zaid
    2019 16TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD), 2019, : 539 - 544
  • [26] Expectation Propagation Aided Signal Detection for Uplink Massive Generalized Spatial Modulation MIMO Systems
    Zhang, Zhenyu
    Gong, Caihong
    Dong, Yuanyuan
    Wang, Xiyuan
    Dai, Xiaoming
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2022, 21 (03) : 2006 - 2018
  • [27] Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System
    Wu, Hebiao
    Shen, Bin
    Zhao, Shufeng
    Gong, Peng
    SENSORS, 2020, 20 (06)
  • [28] Energy Efficient and Low Complexity Signal Detection with Generalized Spatial Modulation for Massive MIMO System Energy Efficient and Low Complexity Signal Detection
    Hanchate, Seema M.
    Nema, Shikha
    2018 3RD INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2018,
  • [29] Low-complexity interference cancellation algorithms for detection in media-based modulated uplink massive-MIMO systems
    Mandloi, Manish
    Gurjar, Devendra Singh
    TELECOMMUNICATION SYSTEMS, 2021, 77 (01) : 129 - 142
  • [30] Low-complexity interference cancellation algorithms for detection in media-based modulated uplink massive-MIMO systems
    Manish Mandloi
    Devendra Singh Gurjar
    Telecommunication Systems, 2021, 77 : 129 - 142