Distributed Multichannel Wiener Filtering for Topology-Unconstrained Wireless Acoustic Sensor Networks

2026-07-06

AI Summary

This work proposes a topology-independent distributed multichannel Wiener filtering algorithm (TI-dMWF) for wireless acoustic sensor networks with arbitrary topologies, aiming to achieve optimal node-specific signal estimation with low communication overhead. By exchanging low-dimensional fused signals, TI-dMWF exactly reproduces the centralized Wiener filter solution in a single run—without requiring iterative refinement or relying on any specific network structure. Theoretical analysis and simulations demonstrate that, under source signal observability conditions, TI-dMWF attains optimality and exhibits strong robustness across reverberant environments, diverse network topologies, and statistical estimation uncertainties. This approach significantly overcomes the limitations of existing distributed filtering methods, which typically depend on iterative procedures and fixed network topologies.

Abstract

This paper introduces the topology-independent distributed multichannel Wiener filter (TI-dMWF), a novel algorithm for distributed node-specific signal estimation in wireless acoustic sensor networks (WASNs) with unconstrained topologies. The TI-dMWF enables each node in the network to compute its centralized multichannel Wiener filter solution by exchanging only low-dimensional fused signals, without requiring iterative estimation, unlike state-of-the-art approaches such as the topology-independent distributed adaptive node-specific signal estimation (TI-DANSE) algorithm. The TI-dMWF is proven optimal when each source is observed by either all nodes or only one node. Theoretical analysis and numerical simulations confirm that it achieves centralized estimation performance in a single run. Its latency as a function of the pruned-tree depth and its computational complexity are also analyzed. Its robustness is assessed in reverberant-room simulations under estimated second-order statistics, various network topologies, and deviations from the assumed observability model.

Problem

• Wireless Acoustic Sensor Networks
• Distributed Signal Estimation
• Multichannel Wiener Filtering
• Topology-Unconstrained Networks
• Node-Specific Estimation

Innovation

• distributed multichannel Wiener filtering
• topology-unconstrained WASNs
• node-specific signal estimation
• non-iterative distributed algorithm
• centralized performance equivalence