Cognitive topology control based on game theory

We have created a framework to design and study distributed topology control algorithms that combine network-formation games with machine learning. The algorithms rely on game players to pursue selfish actions through low-complexity greedy algorithms with low or no signaling overhead. Convergence and stability are ensured through proper mechanism design that eliminates infinite adaptation process. The framework also includes game-theoretic extensions to influence behavior such as fragment merging and preferring links to weakly connected neighbors. Learning allows adaptations that prevent node starvation, reduce link flapping, and minimize routing disruptions by incorporating network layer feedback in cost/utility tradeoffs. Using greedy utility maximization as a benchmark in Telcordia WISER emulator, we show improvements of for metrics such as the numbers of disconnected fragments (14%) and weakly connected nodes (35%), topology stability (41%), and disruption to user flows (16%). The proposed framework is particularly suitable to cognitive radio networks because it can be extended to handle heterogeneous users with different utility functions and conflicting objectives.