Decentralized Bandits with Feedback for Cognitive Radar Networks

TL;DR

This paper introduces a hybrid decentralized cognitive radar network with a central coordinator that improves localization speed by facilitating information exchange among radar nodes, while preserving individual learning capabilities.

Contribution

It proposes a novel hybrid architecture combining centralized coordination with decentralized learning in cognitive radar networks, reducing convergence time for localization tasks.

Findings

Central coordinator reduces localization convergence time.

Radar nodes learn independently while sharing information.

Hybrid structure enhances spectrum utilization in interference-limited environments.

Abstract

Completely decentralized Multi-Player Bandit models have demonstrated high localization accuracy at the cost of long convergence times in cognitive radar networks. Rather than model each radar node as an independent learner, entirely unable to swap information with other nodes in a network, in this work we construct a "central coordinator" to facilitate the exchange of information between radar nodes. We show that in interference-limited spectrum, where the signal to interference plus noise (SINR) ratio for the available bands may vary by location, a cognitive radar network (CRN) is able to use information from a central coordinator to reduce the number of time steps required to attain a given localization error. Importantly, each node is still able to learn separately. We provide a description of a network which has hybrid cognition in both a central coordinator and in each of the cognitive radar nodes, and examine the online machine learning algorithms which can be implemented in this structure.