Distributed Adaptive Learning of Graph Signals
This work addresses distributed signal processing over graphs, which is incremental as it builds on existing graph signal processing methods.
The paper tackles distributed reconstruction of bandlimited graph signals from limited sampled observations, achieving guaranteed mean-square error performance and tracking capability. Numerical results validate the theoretical findings on the proposed distributed adaptive learning method.
The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs.