Apr 26, 2010 · A new approach to estimating sparse (or effectively sparse) multipath channels that is based on some of the recent advances in the theory of compressed sensing.
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This paper proposes sparse channel estimation methods based on convex/linear programming. Quantitative error bounds for the proposed schemes are derived by ...
The main results of this paper adapt recent advances from the theory of compressed sensing to devise quantitative error bounds for convex/linear programming ...
In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing, can potentially achieve a target.
Jun 7, 2023 · We study the channel estimation for wireless communications with movable antenna (MA), which requires to reconstruct the channel response at any location in a ...
By utilizing the fact that a signal is sparse or compressible in some transform domain, CS can powerfully acquire a signal from a small set of randomly ...
BS-IRS-user (cascade) channel. Channel estimation can then be cast as a sparse signal recovery problem and existing compressed-sensing methods can be employed.
Duration: 6:48
Posted: Oct 2, 2020
Posted: Oct 2, 2020
Oct 22, 2024 · In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing (CCS), can potentially achieve ...
The notion of multipath sparsity is formalized and a new approach to estimating sparse multipath channels is presented that is based on some of the recent ...