Game theoretic approach for end-to-end resource allocation in multihop cognitive radio networks

TL;DR

This paper introduces a game theoretic framework for distributed resource allocation in multihop cognitive radio networks, aiming to maximize flow establishment while reducing computational complexity.

Contribution

It proposes three novel game models for resource allocation, demonstrating that a simplified link game achieves near-optimal performance with less information sharing.

Findings

Link game reduces computational complexity

Performance comparable to more complex games

Effective in multihop cognitive radio networks

Abstract

This paper presents a game theoretic solution for end-to-end channel and power allocation in multihop cognitive radio networks analyzed under the physical interference model. The objective is to find a distributed solution that maximizes the number of flows that can be established in the network. The problem is addressed through three different games: a local flow game which uses complete information about the links of the flow, a potential flow game requiring global network knowledge and a cooperative link game based on partial information regarding the links of the flow. Results show that the proposed link game highly decreases the complexity of the channel and power allocation problem in terms of computational load, reducing the information shared between the links forming each flow with a performance similar to that of the more complex flow games.