Self-Boosted Weight-Constrained FxLMS: A Robustness Distributed Active Noise Control Algorithm Without Internode Communication

TL;DR

This paper introduces a novel distributed active noise control algorithm that enhances robustness and reduces resource use by eliminating internode communication, using a self-boosted strategy for local adaptation.

Contribution

The proposed SB-WCFxLMS algorithm improves distributed ANC by removing internode communication and mitigating divergence, with adaptive local constraints for better noise reduction.

Findings

Achieves effective noise cancellation with minimal communication overhead.

Reduces computational complexity compared to centralized methods.

Demonstrates robustness in real acoustic environments.

Abstract

Compared to the conventional centralized multichannel active noise control (MCANC) algorithm, which requires substantial computational resources, decentralized approaches exhibit higher computational efficiency but typically result in inferior noise reduction performance. To enhance performance, distributed ANC methods have been introduced, enabling information exchange among ANC nodes; however, the resulting communication latency often compromises system stability. To overcome these limitations, we propose a self-boosted weight-constrained filtered-reference least mean square (SB-WCFxLMS) algorithm for the distributed MCANC system without internode communication. The WCFxLMS algorithm is specifically designed to mitigate divergence issues caused by the internode cross-talk effect. The self-boosted strategy lets each ANC node independently adapt its constraint parameters based on its local noise reduction performance, thus ensuring effective noise cancellation without the need for inter-node communication. With the assistance of this mechanism, this approach significantly reduces both computational complexity and communication overhead. Numerical simulations employing real acoustic paths and compressor noise validate the effectiveness and robustness of the proposed system. The results demonstrate that our proposed method achieves satisfactory noise cancellation performance with minimal resource requirements.