【文件属性】：
文件名称：Spare Channel Estimation
文件大小：847KB
文件格式：PDF
更新时间：2022-01-09 09:54:32
CS 压缩感知 稀疏信道估计
Compressive sensing is a topic that has
recently gained much attention in the applied
mathematics and signal processing communities.
It has been applied in various areas, such as
imaging, radar, speech recognition, and data
acquisition. In communications, compressive
sensing is largely accepted for sparse channel
estimation and its variants. In this article we
highlight the fundamental concepts of compressive sensing and give an overview of its application to pilot aided channel estimation. We point
out that a popular assumption — that multipath
channels are sparse in their equivalent baseband
representation — has pitfalls. There are overcomplete dictionaries that lead to much sparser
channel representations and better estimation
performance. As a concrete example, we detail
the application of compressive sensing to multicarrier underwater acoustic communications,
where the channel features sparse arrivals, each
characterized by its distinct delay and Doppler
scale factor. To work with practical systems, several modifications need to be made to the compressive sensing framework as the channel
estimation error varies with how detailed the
channel is modeled, and how data and pilot symbols are mixed in the signal design.