Graphical evolutionary game theoretic framework for distributed adaptive filter networks

Most existing distributed adaptive filtering algorithms focus on de- signing different information diffusion rules, regardless of the nature evolutionary characteristic of a distributed network. In this paper, we study the adaptive network from the game theoretic perspective and formulate the distributed adaptive filtering problem as a graphical evolutionary game. For the nodes in the network, the local combination of estimation information from different neighbors is regarded as different strategies selection. We show that this graphical evolutionary game framework is very general and can unify the existing adaptive network algorithms. Based on the framework, as examples, we further propose two error-aware adaptive filtering algorithms. Finally, simulation results are shown to verify the effectiveness of our method.