Distributed Rate-Constrained LCMV Beamforming

Jie Zhang; Andreas I. Koutrouvelis; Richard Heusdens; Richard C. Hendriks

doi:10.1109/LSP.2019.2905161

Distributed Rate-Constrained LCMV Beamforming

Jie Zhang^*, Andreas I. Koutrouvelis, Richard Heusdens, Richard C. Hendriks

^*Corresponding author for this work

Signal Processing Systems

Research output: Contribution to journal › Article › Scientific › peer-review

9 Citations (Scopus)

85 Downloads (Pure)

Abstract

In this letter, we propose a decentralized framework for rate-distributed linearly constrained minimum variance (LCMV) beamforming in wireless acoustic sensor networks. To save the energy usage within the network, we propose to minimize the transmission cost and put a constraint on the noise reduction performance. Subsequently, we decentralize the obtained LCMV filter structure by exploiting an imposed block diagonal form of the noise correlation matrix. As a result, the beamformer weights are calculated in a decentralized fashion and each node can determine its quantization rate locally. Finally, numerical results validate the proposed method.

Original language	English
Article number	8667644
Pages (from-to)	675-679
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	26
Issue number	5
DOIs	https://doi.org/10.1109/LSP.2019.2905161
Publication status	Published - 2019

Keywords

acoustic sensor networks
distributed beamforming
energy usage
LCMV
noise reduction
Rate allocation

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10.1109/LSP.2019.2905161

53544644 - Jie_SPL2019Accepted author manuscript, 130 KB

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AU - Zhang, Jie

AU - Koutrouvelis, Andreas I.

AU - Heusdens, Richard

AU - Hendriks, Richard C.

PY - 2019

Y1 - 2019

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AB - In this letter, we propose a decentralized framework for rate-distributed linearly constrained minimum variance (LCMV) beamforming in wireless acoustic sensor networks. To save the energy usage within the network, we propose to minimize the transmission cost and put a constraint on the noise reduction performance. Subsequently, we decentralize the obtained LCMV filter structure by exploiting an imposed block diagonal form of the noise correlation matrix. As a result, the beamformer weights are calculated in a decentralized fashion and each node can determine its quantization rate locally. Finally, numerical results validate the proposed method.

KW - acoustic sensor networks

KW - distributed beamforming

KW - energy usage

KW - LCMV

KW - noise reduction

KW - Rate allocation

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Distributed Rate-Constrained LCMV Beamforming

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