Compressed Sensing for Wireless Communications: Useful Tips and Tricks
By Jun Won Choi, Byonghyo Shim, Yacong Ding, Bhaskar D. Rao, Dong In Kim. IEEE Communications Surveys and Tutorials, 2015
335 citations recorded by the source corpus.
As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to grasp simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and useful tips and tricks that wireless communication researchers need to know when designing CS-based wireless systems. First, we present an overview of the CS technique, including basic setup, sparse recovery algorithm, and performance guarantee. Then, we describe three distinct subproblems of CS, viz., sparse estimation, support identification, and sparse detection, with various wireless communication applications. We also address main issues encountered in the design of CS-based wireless communication systems. These include potentials and limitations of CS techniques, useful tips that one should be aware of, subtle points that one should pay attention to, and some prior knowledge to achieve better performance. Our hope is that this paper will be a useful guide for wireless communication researchers and even non-experts to get the gist of CS techniques.
Subfields: Computational Mechanics, Computer Networks and Communications, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/98f0cd39691cdac672dc6d14363beb2395ed94c1
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In Computational Mechanics
In Computer Networks and Communications
In Electrical and Electronic Engineering
Citations in the corpus
- Ultra-Reliable and Low-Latency Communications in 5G Downlink: Physical Layer Aspects (2017, methodology): “Since s is a sparse vector and k is known in advance, the information vector s can be recovered via the CS technique [15].”