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Demand-Aware Opportunistic Spectrum Access: A Game-Theoretic Learning Approach Read chapter Read first chapter

2019 | OriginalPaper | Chapter

MSE Analysis Based on Nearly-Oracle Estimation for SCoSaMP Algorithm

Authors : Peng Chen, Cheng Wang, Xiangjun Song, Deliang Liu, Wanling Li

Published in: Communications, Signal Processing, and Systems

Publisher: Springer Singapore

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Abstract

For the reconstruction of signals acquired with Sub-Nyquist sampling system based on redundant Gabor frames, SCoSaMP (Signal space-based CoSaMP) algorithm has excellent performance. However, there’s still no analysis about the MSE analysis under Gaussian noise and it is hard to estimate SCoSaMP algorithm reconstruction performance from a theoretical point of view. This paper presents an MSE analysis method based on nearly-oracle estimation to assess the error generated by Gaussain noise. With the proposed method, the upper bound of the MSE (Mean Square Estimate) is calculated, which shows how to improve the algorithm more quickly.

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Metadata
Title
MSE Analysis Based on Nearly-Oracle Estimation for SCoSaMP Algorithm
Authors
Peng Chen
Cheng Wang
Xiangjun Song
Deliang Liu
Wanling Li
Copyright Year
2019
Publisher
Springer Singapore
Book
Communications, Signal Processing, and Systems

Print ISBN: 978-981-10-6570-5

Electronic ISBN: 978-981-10-6571-2

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-981-10-6571-2_208