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.
Zhu et al. REF propose an adaptive repeated game scheme to ensure the cooperation among nodes in wireless networks and implement a self-learning algorithm to improve cooperation probabilities.
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