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 …

Diffusion adaptation is a powerful strategy for distributed estimation and learning over
networks. Motivated by the concept of combining adaptive filters, this work proposes a …

This work provides a comprehensive overview of adaptive diffusion networks, from the first
papers published on the subject to state-of-the-art solutions and current challenges. These …

The rise of digital and mobile communications has recently made the world more connected
and networked, resulting in an unprecedented volume of data flowing between sources …

This paper studies distributed diffusion adaptation over clustered multi-task networks in the
presence of impulsive interferences and Byzantine attacks. We develop a robust resilient …

Distributed adaptation over multitask networks has attracted particular attention due to its
enhanced modeling capacity compared to that over conventional single-task networks. Most …

Matrices with low numerical rank are omnipresent in many signal processing and data
analysis applications. The pivoted QLP (p-QLP) algorithm constructs a highly accurate …

Combining diffusion strategies with complementary properties enables enhanced
performance when they can be run simultaneously. In this article, we first propose two …

Several practical applications, such as distributed spectrum sensing and channel
identification in underwater communication networks with multiple sensors, can be modeled …

The intelligent wireless sensor network is a distributed network system with high “network
awareness”. Each intelligent node (agent) is connected by the topology within the …