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Robust Diffusion Estimation Over Networks With Noisy Links: Performance Analysis and Optimized Combination Rules
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DOI：10.1109/tsipn.2026.3714234.png)
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Abstract
En 中文
We consider robust distributed estimation over adaptive networks using diffusion strategies in the presence of impulsive measurement noise and noisy inter-node communication links. The underlying diffusion algorithm employs an automatically tuned and adaptive error nonlinearity, enabling robust operation without prior knowledge of the noise statistics. While such robustness effectively mitigates impulsive disturbances in local measurements, the performance of the network can still degrade when the information exchanged among neighboring nodes is corrupted by communication noise. In this work, we analyze the impact of imperfect information exchange on the steady-state performance of robust diffusion adaptation. Closed-form expressions are derived for the network mean-square deviation (MSD) and excess mean-square error (EMSE), explicitly characterizing the contribution of link noise and the role of combination matrix. Based on these results, we develop an optimized combination rule that accounts for both local data statistics and communication noise levels. An adaptive implementation of the proposed rule is further introduced, enabling fully distributed operation with online reliability learning. Simulation results under impulsive noise conditions demonstrate close agreement with the theoretical analysis and show that the proposed adaptive combination strategy significantly improves steady-state performance compared to diffusion schemes with fixed combination weights.
Keywords:
Combination weights
diffusion strategy
distributed estimation
impulsive noise
noisy links
robustness
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724
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