🤖 AI Summary
To address the high computational complexity and excessive control signaling overhead in resource allocation—leading to throughput degradation in multihop cognitive radio networks—this paper proposes a distributed end-to-end joint channel and power allocation mechanism grounded in the physical interference model. The core contribution is a localized cooperative link-level game framework that integrates potential game structure with link-wise coordination strategies, enabling efficient decision-making using only partial state information from neighboring nodes. This design substantially reduces both computational complexity and signaling overhead. Theoretical analysis and simulations demonstrate that the proposed scheme achieves flow admission count and network throughput close to those attainable under centralized, global-information-based game-theoretic optimization. These results validate the effectiveness and feasibility of lightweight distributed optimization for 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.