• DocumentCode
    21885
  • Title

    Detection of Temporally Correlated Signals over Multipath Fading Channels

  • Author

    Huang, Yifei ; Huang, Xiaojing

  • Author_Institution
    CSIRO ICT Centre, Cnr Vimiera and Pembroke Roads, Marsfield, NSW 2122, Australia
  • Volume
    12
  • Issue
    3
  • fYear
    2013
  • fDate
    Mar-13
  • Firstpage
    1290
  • Lastpage
    1299
  • Abstract
    An optimal detection method along with two reduced-complexity methods, modified energy detection (MED) and equal gain detection (EGD), under low signal-to-noise ratio (SNR) condition are proposed in this paper for detection of temporally correlated signals over multipath fading channels. By incorporating resolvable multipaths and multiple antennas into system model, these detection methods are derived based on maximum log-likelihood ratio (LLR) test principal and using the same low SNR LLR approximation. Analytical performance expressions for MED and EGD are also given. Simulation results show that, when signal exhibits temporal correlation, the proposed optimal detection and EGD achieve better performance than conventional generalized likelihood ratio test through utilizing multipath propagation. Further, the proposed MED is superior to conventional energy detection if it a priori signal temporal correlation information is exploited. It is also revealed that multipath tap correlation can have either constructive or destructive effect to spectrum sensing. The proposed EGD is proven to be a practical technique for reliable spectrum sensing over multipath fading channels as it approaches optimal performance with low complexity.
  • Keywords
    Correlation; Fading; Receiving antennas; Sensors; Signal to noise ratio; Vectors; Spectrum sensing; and tap correlation; cognitive radio; maximum likelihood ratio test; multipath fading;
  • fLanguage
    English
  • Journal_Title
    Wireless Communications, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1536-1276
  • Type

    jour

  • DOI
    10.1109/TWC.2013.011713.120748
  • Filename
    6416892