A New Sparse Signal-Matched Measurement Matrix for Compressive Sensing in UWB Communication

被引:22
|
作者
Sharma, Sanjeev [1 ]
Gupta, Anubha [2 ]
Bhatia, Vimal [1 ]
机构
[1] IIT Indore, SaSg, Discipline Elect Engn, Indore 453552, India
[2] Indraprastha Inst Informat Technol Delhi, Dept Elect & Commun Engn, Signal Proc & Biomed Imaging Lab, New Delhi 110020, India
来源
IEEE ACCESS | 2016年 / 4卷
关键词
UWB transmission; compressive sensing; measurement matrix; sparse signal recovery; RECOVERY; ALGORITHMS; IMPACT;
D O I
10.1109/ACCESS.2016.2601779
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Ultra wideband (UWB) technology is suitable for high data rate short range wireless communication, localization, and imaging techniques. However, UWB systems require high sampling rate and precise synchronization. In order to reduce the sampling rate, have precise synchronization, and for low power requirement, UWB systems are implemented using compressive or sub-Nyquist rate measured samples by exploiting the sparsity of the UWB signal. Compressive sensing (CS)-based UWB systems are being designed in two ways: 1) signal demodulation or detection is performed in the CS domain without full signal recovery at the front-end. Thus, demodulation or detection works on compressive measurements. However, system performance deteriorates in the CS domain as compared with full Nyquist rate sampling and 2) after, Nyquist rate signal is recovered using efficient algorithms at the front-end, the signal demodulation or detection is performed using the conventional receiver. Thus, one requires an efficient CS/sampling of signal measurement at the front-end for better system performance for both the cases stated earlier. In this paper, we propose a deterministic (partial) UWB waveform-matched measurement matrix. The proposed measurement matrix has a circulant structure and is sparse in nature. The proposed matrix is easy to implement in hardware and is operationally time efficient as needed in a practical system. The bit error rate performance of the corresponding UWB system and the operational time complexity with the proposed measurement matrix are better as compared with the existing measurement matrices in the CS domain for both the above receiver designs. The efficacy of the proposed measurement matrix is verified through extensive simulations in both the additive white Gaussian noise and multipath communication environments. In addition, we have also compared other desirable properties of the proposed measurement matrix with the existing measurement matrices.
引用
收藏
页码:5327 / 5342
页数:16
相关论文
共 50 条
  • [1] Compressed Sensing Based UWB Receiver Using Signal-Matched Sparse Measurement Matrix
    Sharma, Sanjeev
    Gupta, Anubha
    Bhatia, Vimal
    IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2019, 68 (01) : 993 - 998
  • [2] A COMPRESSIVE SENSING SIGNAL DETECTION FOR UWB RADAR
    Xia, Shugao
    Liu, Yuhong
    Sichina, Jeffrey
    Liu, Fengshan
    PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER, 2013, 141 : 479 - 495
  • [3] Compressive sensing-based signal compression and recovery in UWB wireless communication system
    Wu, Ji
    Wang, Wei
    Liang, Qilian
    Wu, Xiaorong
    Zhang, Baoju
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2014, 14 (13): : 1266 - 1275
  • [4] Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors
    He, Guodong
    Song, Maozhong
    Zhang, Shanshan
    Song, Peng
    Shu, Xinwen
    MATHEMATICAL PROBLEMS IN ENGINEERING, 2020, 2020
  • [5] An Adaptive Sparse Measurement Matrix Design and Selection Strategy for Compressive Sensing SAR
    Li, Tengfei
    Zhang, Qingjun
    Li, Gang
    2016 CIE INTERNATIONAL CONFERENCE ON RADAR (RADAR), 2016,
  • [6] Sparse Measurement Matrix Design and RIP Prove Based on Compressive Sensing in WSN
    Liu, Xiaoyu
    Ling, Yongfa
    Wang, Hui
    2017 INTERNATIONAL CONFERENCE ON COMPUTER SYSTEMS, ELECTRONICS AND CONTROL (ICCSEC), 2017, : 506 - 510
  • [7] Sparse signal recovery with OMP algorithm using sensing measurement matrix
    Gui, Guan
    Mehbodniya, Abolfazl
    Wan, Qun
    Adachi, Fumiyuki
    IEICE ELECTRONICS EXPRESS, 2011, 8 (05): : 285 - 290
  • [8] Distributed sparse signal sensing based on compressive sensing OFDR
    Qu, Shuai
    Qin, Zengguang
    Xu, Yanping
    Liu, Zhaojun
    Cong, Zhenhua
    Wang, Heng
    Li, Zhao
    OPTICS LETTERS, 2020, 45 (12) : 3288 - 3291
  • [9] New measurement matrix for compressive sensing in cognitive radio networks
    Ebian, Ahmed
    Abdelhamid, Bassant
    El-Ramly, Salwa
    IET COMMUNICATIONS, 2018, 12 (11) : 1297 - 1306
  • [10] ON OPTIMIZATION OF THE MEASUREMENT MATRIX FOR COMPRESSIVE SENSING
    Abolghasemi, Vahid
    Ferdowsi, Saideh
    Makkiabadi, Bahador
    Sanei, Saeid
    18TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO-2010), 2010, : 427 - 431