Game theoretic approach for end-to-end resource allocation in multihop cognitive radio networks
Maria Canales, Jorge Ortin, Jose Ramon Gallego
TL;DR
The paper tackles end-to-end resource allocation in multihop cognitive radio networks under a physical interference model by introducing three game formats. Local and potential flow games treat flows as players, achieving end-to-end channel and power decisions but with high information and computation demands; the cooperative link game distributes decisions to individual links, enabling much lower complexity. Simulations show that CLG delivers near-LFG performance and outperforms simple local-link strategies, while reducing information exchange and computation. This work provides a practical, distributed framework for maximizing the number of active flows in CR networks, with potential for analytical convergence enhancements and fairness-focused utility design.
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.