动态调整动量项，让降噪系统更快收敛且更稳定。
Adaptive Momentum Enhanced Distributed Multichannel Active Noise Control for Faster Convergence under Communication Delays
- 用余弦相似度动态调节动量，提升梯度方向一致性
- 在通信延迟下仍保持更快收敛速度与稳定降噪效果
- 适合分布式多通道降噪场景，尤其通信不畅时
分布式多通道主动降噪（DMCANC）通过将处理任务分散到多个节点来降低集中式系统的计算负担，同时需要信息交换以实现良好的全局降噪效果。为提升通信延迟下的鲁棒性，已提出自收缩步长混合梯度滤波参考归一化最小均方（ASSS-MGDFxLMS）算法。然而，减小步长不可避免地导致收敛变慢。本文引入自适应动量项，利用余弦相似度评估瞬时梯度与动量分量之间的对齐程度，并动态调整动量参数。该设计在梯度方向一致时加速收敛，同时在通信延迟下保持稳定性。仿真结果表明，所提出的自适应动量ASSS-MGDFxLMS（AMAS-MGDFxLMS）算法相比ASSS-MGDFxLMS实现了更快的收敛速度，同时维持了稳定有效的降噪性能。
原文摘要 · Abstract (English)
Distributed multichannel active noise control (DMCANC) reduces the computational burden of centralized ANC systems by distributing processing tasks across multiple nodes, while requiring information exchange to achieve satisfactory global noise reduction. To improve robustness under communication delays, the auto-shrink step size mixed-gradients filtered reference LMS (ASSS-MGDFxLMS) algorithm has been proposed. However, the reduced step size inevitably slows convergence. In this work, an adaptive momentum term is introduced to accelerate convergence, where cosine similarity is used to evaluate the alignment between the instantaneous gradient and the momentum component and dynamically adjust the momentum parameter. This design accelerates convergence when the directions are consistent while preserving stability under delayed communication. Simulation results demonstrate that the proposed adaptive momentum ASSS-MGDFxLMS (AMAS-MGDFxLMS) algorithm achieves faster convergence than ASSS-MGDFxLMS while maintaining stable and effective noise reduction performance.
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