Cyclostationary Feature Detection

Cyclostationary Feature Detection is a signal processing technique in cognitive radio systems that exploits the inherent cyclostationarity of communication signals to detect spectral holes by identifying periodic statistical features in the frequency domain, thereby distinguishing man-made transmissions from noise and interference.

149

Publications

8.5K

Citations

394

Authors

200

Institutions

Publications per year

2017–2026

30

Authors

394

  1. DČ
    Danijela Čabrić

    University of California, Los Angeles, United States

    7 publications · 2.9K citations

    7

    h-index

  2. OA
    Octavia A. Dobre

    Memorial University of Newfoundland, Canada

    6 publications · 316 citations

    6

    h-index

  3. ZW
    Zhiqiang Wu

    Wright State University, United States

    5 publications · 114 citations

    5

    h-index

  4. LD
    Linda Doyle

    Trinity College Dublin, Ireland

    4 publications · 657 citations

    4

    h-index

  5. ER
    Eric Rebeiz

    University of California, Los Angeles, United States

    4 publications · 134 citations

    4

    h-index

1–5 of 394

Venues

  1. Mechanical Systems and Signal Processing

    MSSP · 7 publications · 429 citations

    7

    h-index

  2. Signal Processing

    5 publications · 189 citations

    5

    h-index

  3. IEEE Transactions on Wireless Communications

    4 publications · 163 citations

    4

    h-index

  4. IEEE Transactions on Signal Processing

    4 publications · 126 citations

    4

    h-index

  5. IEEE Transactions on Vehicular Technology

    3 publications · 128 citations

    3

    h-index

1–5