Beamforming with Random Projections: Upper and Lower Bounds

TL;DR

This paper introduces a novel data-driven beamforming method using multiple random projections for dimensionality reduction, which improves SNR and SINR gains while balancing computational complexity and interference suppression.

Contribution

It proposes a mixture beamformer based on multiple random projections, providing theoretical bounds and demonstrating improved performance over traditional MVDR beamformers.

Findings

Outperforms MVDR in SNR and SINR gains

Provides upper and lower bounds for compressed beamformer output power

Balances computational complexity with interference suppression

Abstract

Beamformers often trade off white noise gain against the ability to suppress interferers. With distributed microphone arrays, this trade-off becomes crucial as different arrays capture vastly different magnitude and phase differences for each source. We propose the use of multiple random projections as a first-stage preprocessing scheme in a data-driven approach to dimensionality reduction and beamforming. We show that a mixture beamformer derived from the use of multiple such random projections can effectively outperform the minimum variance distortionless response (MVDR) beamformer in terms of signal-to-noise ratio (SNR) and signal-to-interferer-and-noise ratio (SINR) gain. Moreover, our method introduces computational complexity as a trade-off in the design of adaptive beamformers, alongside noise gain and interferer suppression. This added degree of freedom allows the algorithm to better exploit the inherent structure of the received signal and achieve better real-time performance while requiring fewer computations. Finally, we derive upper and lower bounds for the output power of the compressed beamformer when compared to the full complexity MVDR beamformer.