research-article

Compressed Sensing for Wireless Communications: Useful Tips and Tricks

Authors:

Dong In KimAuthors Info & Claims

IEEE Communications Surveys & Tutorials, Volume 19, Issue 3

Pages 1527 - 1550

https://doi.org/10.1109/COMST.2017.2664421

Published: 01 July 2017 Publication History

Abstract

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.

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cover image IEEE Communications Surveys & Tutorials

IEEE Communications Surveys & Tutorials Volume 19, Issue 3

thirdquarter 2017

647 pages

ISSN:1553-877X

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1553-877X © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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IEEE Press

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Published: 01 July 2017

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Kim SWu JShim B(2024)Efficient Channel Probing and Phase Shift Control for mmWave Reconfigurable Intelligent Surface-Aided CommunicationsIEEE Transactions on Wireless Communications10.1109/TWC.2023.327697523:1(231-246)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TWC.2023.3276975
Wipf DKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Marginalization is not marginalProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619954(37108-37132)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619954
Marata LLuis Alcaraz López ODjelouat HLeinonen MAlves HJuntti M(2023)Joint Coherent and Non-Coherent Detection and Decoding Techniques for Heterogeneous NetworksIEEE Transactions on Wireless Communications10.1109/TWC.2022.320660822:3(1730-1744)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1109/TWC.2022.3206608
Zheng BZeng CLi SLiao G(2023)Recovery Guarantee Analyses of Joint Sparse Recovery via Tail $ \ell _{2,1}$ MinimizationIEEE Transactions on Signal Processing10.1109/TSP.2023.327955771(4342-4352)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TSP.2023.3279557
Nazzal MAli Aygül MArslan H(2022)Estimation and Exploitation of Multidimensional Sparsity for MIMO-OFDM Channel Estimation2022 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC51071.2022.9771576(980-985)Online publication date: 10-Apr-2022
https://dl.acm.org/doi/10.1109/WCNC51071.2022.9771576
Qiao LZhang JGao ZNg DRenzo MAlouini M(2022)Massive Access in Media Modulation Based Massive Machine-Type CommunicationsIEEE Transactions on Wireless Communications10.1109/TWC.2021.309548421:1(339-356)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1109/TWC.2021.3095484
Žarić MDraganić AOrović IBeko MStanković S(2022)Combining Gradient-Based and Thresholding Methods for Improved Signal Reconstruction PerformanceJournal of Signal Processing Systems10.1007/s11265-022-01780-595:5(643-656)Online publication date: 24-Jun-2022
https://dl.acm.org/doi/10.1007/s11265-022-01780-5
Meng NZhao YKočvara MSun Z(2022)Partial gradient optimal thresholding algorithms for a class of sparse optimization problemsJournal of Global Optimization10.1007/s10898-022-01143-184:2(393-413)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1007/s10898-022-01143-1
Xifilidis TPsannis K(2022)Correlation-based wireless sensor networks performance: the compressed sensing paradigmCluster Computing10.1007/s10586-021-03480-425:2(965-981)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s10586-021-03480-4
Cui HAbdelzaher T(2021)SenseLens: An Efficient Social Signal Conditioning System for True Event DetectionACM Transactions on Sensor Networks10.1145/348504718:2(1-27)Online publication date: 29-Oct-2021
https://dl.acm.org/doi/10.1145/3485047
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