Distributed Recursive Least Squares Strategies for Adaptive Reconstruction of Graph Signals

Conference paper (2017)

Authors

Paolo Di Lorenzo University of Perugia

E. Isufi Signal Processing Systems -

P Banelli University of Perugia

Sergio Barbarossa Sapienza University of Rome

G.J.T. Leus Signal Processing Systems -

Research Group

Signal Processing Systems () (TU Delft)

Sampling Graph signal processing Adaptive networks Recursive least squares estimation

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Published Date

2017

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

Abstract

This work proposes distributed recursive least squares (RLS) strategies for adaptive reconstruction and learning of signals defined over graphs. First, we introduce a centralized RLS estimation strategy with probabilistic sampling, and we propose a sparse sensing method that selects the sampling probability at each node in the graph in order to guarantee adaptive signal reconstruction and a target steady-state performance. Then, a distributed RLS strategy is derived and is shown to be convergent to its centralized counterpart. The performed numerical tests show the performance of the proposed adaptive method for distributed learning of graph signals.

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