Variable Step-Size Diffusion Bias-Compensated APV Algorithm Over Networks

doi:10.1109/TSIPN.2024.3496255

Variable Step-Size Diffusion Bias-Compensated APV Algorithm Over Networks

2024-01-01

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DOI：10.1109/TSIPN.2024.3496255

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This paper investigates the distributed estimation problem over networks with highly correlated and noisy inputs. As a first step, this paper proposes an algorithm based on diffusion affine projection Versoria (APV) that can process highly correlated input signals over networks. Following that, the optimal step-size is derived by minimizing the mean-square deviation at each node, so that the tradeoff between convergence rate and steady-state error can be addressed. To reduce estimation bias caused by input noise, two diffusion bias-compensated APV (DBCAPV) algorithms are then developed by solving the asymptotic unbiasedness or local constrained optimization problems. Using the optimal step-size processed through the moving average and reset mechanisms, two variable step-size DBCAPV algorithms are obtained. The simulation results demonstrate that our methods are effective.

Keyword:

Noise measurement

Vectors

Convergence

Noise

Steady-state

Estimation

Information processing

Adaptive systems

Robustness

Decorrelation

Adaptive networks

affine projection

bias compensation

distributed etimation

variable step-size

Variable Step-Size Diffusion Bias-Compensated APV Algorithm Over Networks

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