Publication Date:
2022-05-25
Description:
Author Posting. © IEEE, 2009. This article is posted here by permission of IEEE for personal use, not for redistribution. The definitive version was published in IEEE Transactions on Signal Processing 58 (2010): 1708-1721, doi:10.1109/TSP.2009.2038424.
Description:
In this paper, we investigate various channel estimators
that exploit channel sparsity in the time and/or Doppler
domain for a multicarrier underwater acoustic system. We use a
path-based channel model, where the channel is described by a
limited number of paths, each characterized by a delay, Doppler
scale, and attenuation factor, and derive the exact inter-carrierinterference
(ICI) pattern. For channels that have limited Doppler
spread we show that subspace algorithms from the array processing
literature, namely Root-MUSIC and ESPRIT, can be applied
for channel estimation. For channels with Doppler spread, we
adopt a compressed sensing approach, in form of Orthogonal
Matching Pursuit (OMP) and Basis Pursuit (BP) algorithms, and
utilize overcomplete dictionaries with an increased path delay
resolution. Numerical simulation and experimental data of an
OFDM block-by-block receiver are used to evaluate the proposed
algorithms in comparison to the conventional least-squares (LS)
channel estimator.We observe that subspace methods can tolerate
small to moderate Doppler effects, and outperform the LS
approach when the channel is indeed sparse. On the other hand,
compressed sensing algorithms uniformly outperform the LS and
subspace methods. Coupled with a channel equalizer mitigating
ICI, the compressed sensing algorithms can effectively handle
channels with significant Doppler spread.
Description:
C. Berger, S. Zhou, and P. Willett are supported by ONR
grants N00014-09-10613, N00014-07-1-0805, and N00014-09-1-0704.
Keywords:
Basis Pursuit
;
Doppler spread
;
ESPRIT
;
ICI
;
MUSIC
;
OFDM
;
Orthogonal Matching Pursuit
Repository Name:
Woods Hole Open Access Server
Type:
Article
Format:
application/pdf
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