Maximum Likelihood Channel Estimation

Maximum Likelihood Channel Estimation is a parametric estimation method that belongs to the class of statistical inference techniques in signal processing, distinguished by its use of maximizing the likelihood function of observed data to obtain the most probable channel parameters under assumed noise and signal models.

470

Publications

19K

Citations

916

Authors

373

Institutions

Publications per year

2017–2026

55

Variants

Mlce

Authors

916

  1. DS
    Dirk Slock

    EURECOM, France

    8 publications · 483 citations

    8

    h-index

  2. JP
    J.G. Proakis

    Northeastern University, United States

    6 publications · 179 citations

    6

    h-index

  3. BH
    Babak Hassibi

    California Institute of Technology, United States

    6 publications · 162 citations

    6

    h-index

  4. TC
    Tao Cui

    California Institute of Technology, United States

    6 publications · 181 citations

    6

    h-index

  5. UM
    Urbashi Mitra

    University of Southern California, United States

    6 publications · 163 citations

    6

    h-index

1–5 of 916

Venues

  1. IEEE Transactions on Communications

    54 publications · 3.3K citations

    31

    h-index

  2. IEEE Transactions on Signal Processing

    44 publications · 2.9K citations

    26

    h-index

  3. IEEE Transactions on Wireless Communications

    35 publications · 1.5K citations

    22

    h-index

  4. IEEE Transactions on Vehicular Technology

    28 publications · 1.7K citations

    18

    h-index

  5. IEEE Communications Letters

    21 publications · 717 citations

    15

    h-index

1–5