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

2019 | OriginalPaper | Chapter

Widely Linear Adaptive Beamforming Algorithm Based on Minimum Sensitivity and Eigenspace

Authors : Liping Huo, Xingpeng Mao, Liang Xin, Yunmei Shi, Guangyan Li

Published in: Communications, Signal Processing, and Systems

Publisher: Springer Singapore

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Abstract

The conventional minimum variance distortionless response (MVDR) beamformer becomes suboptimal for the noncircular signals, and deteriorates when the training samples are limited. The eigenspace-based (ESB) WL MVDR beamformer is proposed, which utilizes the eigenstructure of the correlation matrix to enhance the performance of the WL MVDR beamformer. Further, an eigenspace-based widely linear beamformer for noncircular signals using the minimum sensitivity criterion is proposed, which can be used for reducing the performance degradation when the dimension of the SI subspace can not be estimated correctly. Simulation results show that the proposed method has a better performance.

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Metadata
Title
Widely Linear Adaptive Beamforming Algorithm Based on Minimum Sensitivity and Eigenspace
Authors
Liping Huo
Xingpeng Mao
Liang Xin
Yunmei Shi
Guangyan 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_95