Subspace-based Channel Estimation

Subspace-Based Channel Estimation is a channel estimation method that exploits the orthogonality between signal and noise subspaces, derived from the eigen-decomposition of the received signal's covariance matrix, to separate and estimate the underlying channel parameters with enhanced resolution and reduced noise sensitivity.

304

Publications

15.6K

Citations

588

Authors

261

Institutions

Publications per year

2017–2026

38

Authors

588

  1. ZX
    Zhengyuan Xu

    University of Science and Technology of China, China

    9 publications · 312 citations

    9

    h-index

  2. AP
    A. Paulraj

    Stanford University, United States

    9 publications · 723 citations

    9

    h-index

  3. LT
    Lang Tong

    Cornell University, United States

    7 publications · 915 citations

    7

    h-index

  4. GB
    Georgios B. Giannakis

    University of Minnesota, United States

    7 publications · 597 citations

    7

    h-index

  5. US
    Umberto Spagnolini

    Politecnico di Milano, Italy

    6 publications · 382 citations

    6

    h-index

1–5 of 588

Venues

  1. IEEE Transactions on Signal Processing

    62 publications · 4.9K citations

    33

    h-index

  2. IEEE Transactions on Wireless Communications

    25 publications · 1.3K citations

    19

    h-index

  3. IEEE Transactions on Communications

    19 publications · 2.1K citations

    16

    h-index

  4. IEEE Signal Processing Letters

    13 publications · 324 citations

    11

    h-index

  5. IEEE Transactions on Vehicular Technology

    13 publications · 751 citations

    11

    h-index

1–5