Adaptive Improved Primal-Dual Distributed Stochastic Gradient Driven Distributed Active Noise Control System

Distributed active noise control (DANC) systems, typically employing the filtered-x least mean squares algorithm, are widely used for spatial noise reduction in acoustic networks. However, due to acoustic coupling among the nodes, vulnerability to impulsive interference, and degradation in non-Gaussian noise environments, the performance of these systems degrades. Additionally, most of the existing DANC frameworks neglect practical limitations in communication, such as transmission delays and interruptions, which limits their scalability in real-world deployments. To address these challenges, an adaptive improved distributed stochastic gradient (AIDSG) algorithm grounded within the primal-dual optimization framework is introduced in this paper. The proposed approach initiates an informed primal-dual update across nodes with instantaneous dual feedback, which results in resilience against colored and impulsive noise, accelerates convergence, and increases adaptability to dynamically changing acoustic environments. Four types of algorithm variants are presented, which address the trade-off between convergence speed and steady-state error. Simulation results on a five-node network validate the superiority of the AIDSG algorithm over conventional DANC and diffusion-based schemes while demonstrating better residual reduction performance, faster mean square convergence, and a better mean noise ratio under additive white Gaussian noise, colored noise, and impulsive noise conditions.