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Iterative Order Recursive Least Square Estimation for Exploiting Frame-Wise Sparsity in Compressive Sensing-Based MTC

Authors
Abebe, Ameha T.Kang, Chung G.
Issue Date
5월-2016
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Orthogonal matching pursuit (OMP); machine type communication (MTC); group orthogonal matching pursuit (GOMP); iterative order-recursive least square (IORLS); compressive sensing (CS)
Citation
IEEE COMMUNICATIONS LETTERS, v.20, no.5, pp.1018 - 1021
Indexed
SCIE
SCOPUS
Journal Title
IEEE COMMUNICATIONS LETTERS
Volume
20
Number
5
Start Page
1018
End Page
1021
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/88847
DOI
10.1109/LCOMM.2016.2539255
ISSN
1089-7798
Abstract
In multiple measurement vectors (MMV) problems, the sparsity structure, i.e., the support of the measurement vectors, remains constant for multiple instants. For machine type communication (MTC) context, this sparsity structure may remain constant over all symbols in a frame, which can be termed as frame-wise sparsity. Instead of employing symbol-by-symbol detection based on algorithms such as orthogonal matching pursuit (OMP), group orthogonal matching pursuit (GOMP) can take advantage of this constant sparsity structure and decodes group of symbols together in order to improve the accuracy. Unfortunately, the exponential growth in computational complexity of the GOMP algorithm with the group size prohibits it from increasing the group size and fully exploiting the frame-wise sparsity. This letter presents an iterative order recursive least square (IORLS) algorithm, which can exploit the frame-wise sparsity and increase accuracy. IORLS iteratively employs a modified OMP operations over a frame to gather the sparsity support information with manageable complexity. IORLS substantially reduces complexity by avoiding the matrix inversions in OMP and GOMP algorithms. Furthermore, it has been shown that the proposed algorithm is robust against noise, achieving near-oracle estimation performance.
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공과대학 (전기전자공학부)
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