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Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
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BibTeX
@MISC{Bajwa_compressedchannel,
author = {Waheed U. Bajwa and Jarvis Haupt and Akbar M. Sayeed and Robert Nowak},
title = {Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels},
year = {}
}
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Abstract
High-rate data communication over a multipath wireless channel often requires that the channel response be known at the receiver. Training-based methods, which probe the channel in time, frequency, and space with known signals and reconstruct the channel response from the output signals, are most commonly used to accomplish this task. Traditional training-based channel estimation methods, typically comprising of linear reconstruction techniques, are known to be optimal for rich multipath channels. However, physical arguments and growing experimental evidence suggest that many wireless channels encountered in practice tend to exhibit a sparse multipath structure that gets pronounced as the signal space dimension gets large (e.g., due to large bandwidth or large number of antennas). In this paper, we formalize the notion of multipath sparsity and present 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. In particular, it is shown in the paper that the proposed approach, which is termed as compressed channel sensing, can potentially achieve a target reconstruction error using far less energy and, in many instances, latency and bandwidth than that dictated by the traditional least-squares-based training methods.
Keyphrases
new approach estimating sparse multipath channel channel response multipath wireless channel compressed channel sensing large bandwidth output signal traditional least-squares-based training method large number physical argument traditional training-based channel estimation method high-rate data communication many wireless channel compressed sensing sparse multipath structure target reconstruction error many instance experimental evidence training-based method signal space dimension recent advance rich multipath channel multipath channel multipath sparsity linear reconstruction technique
