Analysis of distributed active noise control systems

Active noise control technology has witnessed a great success in the past decades for local noise control in headsets and similar devices. However, many challenges are yet to be tackled for active control of noise over a large spatial region, where multichannel systems are needed. One of the significant issues is the high computational complexity of multichannel adaptive algorithms running on a centralized processor. To overcome this problem, distributed and decentralized adaptation strategies have been proposed to spread the computational burden over multiple processors recently. This paper will review existing distributed and decentralized active noise control systems based on the filtered-reference least mean square algorithms and analyze their convergence behavior under a unified framework. The convergence conditions in the mean and mean-square sense are studied and the transient and stead-state mean square errors are also analyzed. Simulations are performed to verify the proposed framework and analysis. This work will provide insights into current distributed active noise control systema and shed light on developing new algorithm design in the future.