Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
Source abstract
Abstract
With the development of intelligent networks such as the Internet of Things, network scales are becoming increasingly larger, and network environments increasingly complex, which brings a great challenge to network communication. The issues of energy-saving, transmission efficiency, and security were gradually highlighted. Compressed sensing (CS) helps to simultaneously solve those three problems in the communication of intelligent networks. In CS, fewer samples are required to reconstruct sparse or compressible signals, which breaks the restrict condition of a traditional Nyquist–Shannon sampling theorem. Here, we give an overview of recent CS studies, along the issues of sensing models, reconstruction algorithms, and their applications. First, we introduce several common sensing methods for CS, like sparse dictionary sensing, block-compressed sensing, and chaotic compressed sensing. We also present several state-of-the-art reconstruction algorithms of CS, including the convex optimization, greedy, and Bayesian algorithms. Lastly, we offer recommendation for broad CS applications, such as data compression, image processing, cryptography, and the reconstruction of complex networks. We discuss works related to CS technology and some CS essentials.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sparse and Compressive Sensing Techniques · Advanced MRI Techniques and Applications · Microwave Imaging and Scattering Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
School of Computer Science and Technology, Henan Polytechnic University, 2001 Century Avenue, Jiaozuo 454003, China
Potsdam Institute for Climate Impact Research, D14473 Potsdam, Germany
Guizhou University, Guizhou Provincial Key Laboratory of Public Big Data, Guiyang 550025, China
Funding
National Natural Science Foundation of China
References (90)
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