A novel method of blind signal detection using the distribution of the bin values of the power spectrum density and the moving average

被引:9
|
作者
Nikonowicz, Jakub [1 ]
Jessa, Mieczyslaw [1 ]
机构
[1] Poznan Univ Tech, Fac Elect & Telecommun, PL-60965 Poznan, Poland
关键词
Blind signal detection; Power density spectrum; Moving average; Gaussian noise; COGNITIVE RADIO; ENERGY DETECTION; SELECTION;
D O I
10.1016/j.dsp.2017.04.002
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Signal detection in additive white Gaussian noise (AWGN) is one of the long-term developments driving the evolution of many different fields of science and technology, with important applications in telecommunications, medicine and astronomy. In this paper, we propose a novel method of blind signal detection that does not require knowledge of the noise variance. This method uses the distribution of the bin values of the power spectrum density of the received signal and the moving average (MAV). The simulation results for radio pulses show that the spectrum sensing performance is significantly improved under the proposed scheme compared to that of known blind signal detection methods. (C) 2017 Elsevier Inc. All rights reserved.
引用
收藏
页码:18 / 28
页数:11
相关论文
共 50 条
  • [1] A method for blind detection of OFDM signal based on power spectrum reprocessing
    Zhang Hai-Ying
    Yuan Chao-Wei
    SNPD 2007: EIGHTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING, AND PARALLEL/DISTRIBUTED COMPUTING, VOL 2, PROCEEDINGS, 2007, : 181 - +
  • [2] A Blind Spectrum Sensing Method for DTV Signal Detection
    Subekti, Agus
    Sugihartono, Sugi
    Rachmana, Nana S.
    Bayu, Andriyan S.
    2013 INTERNATIONAL CONFERENCE OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICOICT), 2013, : 269 - 272
  • [3] Prediction of Signal Characteristics Using Autoregressive Moving Average Method
    Guluyev, Gambar
    Pashayev, Fahrad
    Pakdel, Majid
    Sattarova, Ulkar
    2012 IV INTERNATIONAL CONFERENCE PROBLEMS OF CYBERNETICS AND INFORMATICS (PCI), 2012,
  • [4] Detection of modulated chatter using moving average difference spectrum analysis
    Xu, Xiaoqiang
    Zhou, Tianyu
    Wan, Liyou
    Hu, Hongwei
    Hu, Yongle
    JOURNAL OF SOUND AND VIBRATION, 2022, 517
  • [5] Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation
    Peizhe Xin
    Ying Liu
    Nan Yang
    Xuankun Song
    Yu Huang
    Global Energy Interconnection, 2020, 3 (03) : 247 - 258
  • [6] Probability distribution of wind power volatility based on the moving average method and improved nonparametric kernel density estimation
    Xin P.
    Liu Y.
    Yang N.
    Song X.
    Huang Y.
    Global Energy Interconnection, 2020, 3 (03) : 247 - 258
  • [7] A moving window average method for internal fault detection of power transformers
    Taheri, Behrooz
    Sedighizadeh, Mostafa
    CLEANER ENGINEERING AND TECHNOLOGY, 2021, 4
  • [8] A novel adaptive moving average method for signal denoising in strong noise background
    Shan, Zhen
    Yang, Jianhua
    Sanjuan, Miguel A. F.
    Wu, Chengjin
    Liu, Houguang
    EUROPEAN PHYSICAL JOURNAL PLUS, 2021, 137 (01):
  • [9] A novel adaptive moving average method for signal denoising in strong noise background
    Zhen Shan
    Jianhua Yang
    Miguel A. F. Sanjuán
    Chengjin Wu
    Houguang Liu
    The European Physical Journal Plus, 137
  • [10] Blind Spectrum Sensing Method for OFDM Signal Detection in Cognitive Radio Communications
    Prema, G.
    Gayatri, P.
    2014 INTERNATIONAL CONFERENCE ON COMMUNICATION AND NETWORK TECHNOLOGIES (ICCNT), 2014, : 42 - 47