Channel Estimation and Pilot Overhead Reduction in OFDM Systems Using Compressed Sensing Dynamic Mode Decomposition

被引：0
作者：
Haddad, Fayad [1 ]
Bockelmann, Carsten [1 ]
Dekorsy, Armin [1 ]
机构：
[1] Univ Bremen, Dept Commun Engn, D-28359 Bremen, Germany
来源：
IEEE COMMUNICATIONS LETTERS | 2024年 / 28卷 / 05期
关键词：
Channel estimation; OFDM; Symbols; Compressed sensing; Interpolation; Estimation; Wireless communication; compressed sensing; data-driven methods; dynamic mode decomposition;
D O I：
10.1109/LCOMM.2024.3371105
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
This work investigates the potential of employing the approach Compressed Sensing Dynamic Mode Decomposition (CS-DMD) in the context of time-varying wireless channels. To the best of the authors' knowledge, this marks the first instance of utilizing CS-DMD for pilot-based channel estimation in Orthogonal Frequency Division Multiplexing (OFDM) systems. The effectiveness of this method is compared with two advanced deep learning-based channel estimation techniques: Interpolation-ResNet and Learned Approximate Message Passing (LAMP). Furthermore, we leverage the advantageous characteristics of DMD in analyzing complex nonlinear dynamic systems to predict the future state of the channel, thereby reducing the required pilot signals. Simulation results show that utilizing CS-DMD can achieve superior channel estimation performance with less pilot overhead.
引用
收藏
页码：1137 / 1140
页数：4
相关论文
共 50 条
  • [31] Channel Estimation Algorithm Based on Compressed Sensing for Underwater Acoustic OFDM Communication System
    Zou, Zhiqiang
    Lu, Siyu
    Shen, Shu
    Wang, Ruchuan
    Lin, Xiangyu
    ADVANCES IN WIRELESS SENSOR NETWORKS, 2015, 501 : 114 - 124
  • [32] A compressed sensing technique for OFDM channel estimation in mobile environments:: Exploiting channel sparsity for reducing pilots
    Tauboeck, Georg
    Hlawatsch, Franz
    2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12, 2008, : 2885 - 2888
  • [33] Compressed sensing based channel estimation using group random unit pilot for OFDM systems in fast moving environment
    Fang Y.
    Zhou X.
    Wang M.
    High Technology Letters, 2011, 17 (02) : 113 - 118
  • [34] Compressed Sensing Based Channel Estimation for OFDM Transmission under 3GPP Channels
    Wang, Han
    Du, Wencai
    Bai, Yong
    INTERNATIONAL JOURNAL OF FUTURE GENERATION COMMUNICATION AND NETWORKING, 2016, 9 (04): : 85 - 93
  • [35] Implementation of compressed sensing technology with ROMP reconstruction for sparse channel estimation in OFDM systems
    Hui L.
    Nan J.
    Hui Y.D.
    Advances in Information Sciences and Service Sciences, 2011, 3 (08): : 132 - 138
  • [36] Massive MIMO-OFDM Channel Estimation via Distributed Compressed Sensing
    Akbarpour-Kasgari, Abbas
    Ardebilipour, Mehrdad
    IEEE WIRELESS COMMUNICATIONS LETTERS, 2019, 8 (02) : 376 - 379
  • [37] Estimation of Underwater Acoustic MIMO-OFDM Channel Based on Compressed Sensing
    Ren, Xinmin
    Zhao, Min
    Hou, Jing
    Long, Xuguang
    Yang, Zongwei
    2019 IEEE UNDERWATER TECHNOLOGY (UT), 2019,
  • [38] CHANNEL ESTIMATION IN UWB CHANNELS USING COMPRESSED SENSING
    Cohen, Kfir M.
    Attias, Chen
    Farbman, Barak
    Tselniker, Igor
    Eldar, Yonina C.
    2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2014,
  • [39] Pilot Embedded Channel Estimation for OFDM Systems
    Yang, Weiwei
    Cai, Yueming
    Cheng, Yunpeng
    2007 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-15, 2007, : 81 - 84
  • [40] Pilot Allocation for Distributed-Compressed-Sensing-Based Sparse Channel Estimation in MIMO-OFDM Systems
    He, Xueyun
    Song, Rongfang
    Zhu, Wei-Ping
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2016, 65 (05) : 2990 - 3004
12345