Distributed Robust Beamforming Based on Low-Rank and Cross-Correlation Techniques: Design and Analysis

In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between th...

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Bibliographic Details
Published in:IEEE transactions on signal processing 2019-12, Vol.67 (24), p.6411-6423
Main Authors: Ruan, Hang, de Lamare, Rodrigo C.
Format: Article
Language:English
Subjects:
Algorithms
Array signal processing
Beamforming
Channel estimation
Computer simulation
Correlation analysis
Covariance matrices
Eigenvalues and eigenfunctions
Interference
Optimization
Perturbation methods
Relays
Robust distributed beamforming
Signal to noise ratio
SINR maxi-mization
subspace projection techniques
Wireless networks
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Summary:In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.
ISSN:1053-587X
1941-0476
DOI:10.1109/TSP.2019.2954519