Abstract-For the networks with packet forwarding, distributed control to enforce cooperation for nodes' packet forwarding probabilities is essential to maintain the connectivity of networks. In this paper, we propose a novel self-learning repeated game framework to optimize packet forwarding probabilities of distributed users. The framework has two major steps: First, an adaptive repeated game scheme ensures the cooperation among users for the current cooperative packet forwarding probabilities. Second, a self-learning scheme tries to find better cooperation probabilities. Some special cases are analyzed to evaluate the proposed framework. From the simulation results, the proposed framework demonstrates the near optimal solutions in both symmetry and asymmetry network.
An adaptive repeated game theoretic scheme was designed in REF for wireless networks to ensure the cooperations among nodes, and then a self-learning algorithm was employed to find the optimal cooperation probability.
A self-learning repeated game framework for optimizing packet forwarding networks
IEEE Wireless Communications and Networking Conference, 2005
IEEE Wireless Communications and Networking Conference, 2005
Computer Science