Distributed Greedy Pursuit Algorithms

TL;DR

This paper introduces new distributed greedy algorithms for compressed sensing over networks, leveraging a novel signal model and modifications of existing algorithms to achieve near-centralized performance with low communication costs.

Contribution

It develops and combines modified orthogonal matching pursuit and subspace pursuit algorithms into new distributed methods for sparse signal recovery.

Findings

New algorithms perform close to centralized solutions.

Effective in sparsely connected networks.

Low communication overhead.

Abstract

For compressed sensing over arbitrarily connected networks, we consider the problem of estimating underlying sparse signals in a distributed manner. We introduce a new signal model that helps to describe inter-signal correlation among connected nodes. Based on this signal model along with a brief survey of existing greedy algorithms, we develop distributed greedy algorithms with low communication overhead. Incorporating appropriate modifications, we design two new distributed algorithms where the local algorithms are based on appropriately modified existing orthogonal matching pursuit and subspace pursuit. Further, by combining advantages of these two local algorithms, we design a new greedy algorithm that is well suited for a distributed scenario. By extensive simulations we demonstrate that the new algorithms in a sparsely connected network provide good performance, close to the performance of a centralized greedy solution.