Problem
Research questions and friction points this paper is trying to address.
average consensus
noisy communication
martingale difference sequence
decentralized gradient descent
disturbance accumulation
Exact Average Consensus under Noisy Communication Links: A Decentralized Gradient Perspective
🤖 AI Summary
研究在噪声通信链路下通过基于梯度下降的锚定机制实现精确平均共识的方法，解决了标准方法只能达成非零方差随机变量共识的问题。
📝 Abstract
We study the distributed average consensus problem under persistent link-level disturbances modeled as a martingale difference sequence with uniformly bounded conditional second moments. Under such disturbances, the standard stochastic-approximation-based linear iteration with diminishing stepsizes drives the network to consensus on an unbiased random variable with non-vanishing variance instead of the exact initial average. To understand and resolve this limitation, we develop an anchoring-based mechanism derived from a decentralized gradient descent formulation and study the effect of incorporating a decaying anchoring term that continuously pulls each agent state toward its initial value. This perspective provides an intuitive interpretation of how state anchoring counteracts disturbance accumulation. Under standard summability conditions, we prove that the resulting algorithm achieves exact average consensus almost surely. Furthermore, this decentralized gradient perspective offers a unifying framework for several related methods and an interpretable design principle for exact average consensus under persistent disturbances.
Innovation
Methods, ideas, or system contributions that make the work stand out.
anchoring-based mechanism
decentralized gradient descent
exact average consensus
persistent disturbances
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