Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
Compressed Channel Sensing: A New Approach to Estimating Sparse Multipath Channels
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DOI：
10.1109/jproc.2010.2042415
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发表时间：
2010-06-01
影响因子：
20.6
通讯作者：
Nowak, Robert
中科院分区：
文献类型：
--
作者：
Bajwa, Waheed U.;Haupt, Jarvis;Nowak, Robert
多径无线信道上的高速率数据通信通常要求在接收机处知道信道响应。基于训练的方法是最常用于完成此任务的方法，该方法利用已知信号在时间、频率和空间上探测信道，并从输出信号重建信道响应。已知传统的基于训练的信道估计方法（通常包括线性重构技术）对于丰富的多径信道是最佳的。然而，物理参数和越来越多的实验证据表明，在实践中遇到的许多无线信道往往表现出稀疏的多径结构，随着信号空间维度变大而变得明显（例如，例如，在一个实施例中，由于大带宽或大量天线）。在本文中，我们正式的多径稀疏的概念，并提出了一种新的方法来估计稀疏（或有效稀疏）的多径信道，这是基于压缩感知理论的一些最新进展。特别是，它示出在该文件中，所提出的方法，这被称为压缩信道感知（CCS），可以潜在地实现目标的重建误差使用更少的能量，在许多情况下，延迟和带宽比传统的基于最小二乘的训练方法所规定的。
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 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 (CCS), 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.