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Improved Diffusion Recursive Least Squares for Graph Signal Estimation on Distributed Network
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DOI：10.1109/TSIPN.2025.3648322.png)
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Abstract
En 中文
Streaming graph signal (GS) estimation is common in various network systems. Several graph filter algorithms have been proposed for streaming GS estimation, but they still fail to reach optimal levels. To achieve optimal performance in both estimation accuracy and convergence rate, this paper adopts the recursive least squares (RLS) method in processing GS. When the RLS algorithm is directly combined with GS, its recursive mechanism causes the estimation performance to experience severe degradation. To address this issue, a graph RLS with non-cooperation algorithm and a distributed graph diffusion RLS (DRLS) algorithm, both following the fully recursive structure of the standard RLS, are proposed first. By analyzing these two algorithms, it is found that streaming GS and graph topology are complex and variable, so the previous recursive mechanism is not suitable. Therefore, a dynamic adaptive recursive mechanism is designed, and based on this, a distributed graph improved DRLS (IDRLS) algorithm is proposed. Convergence analysis confirms that the proposed algorithm achieves mean stability and mean-square convergence at a linear rate. Furthermore, we thoroughly examine the causes of performance degradation and demonstrate the superiority of the distributed graph IDRLS algorithm. Finally, experiments, conducted on two different graphs with different levels of sparsity and real-world dataset, verify that the proposed graph IDRLS algorithm can achieve the superior estimation performance and convergence rate and be more effective than the related graph algorithms.
Keywords:
Distributed algorithm
recursive least squares
graph filter
streaming graph signal
diffusion strategy
Journal
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727
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