Distributed Adaptive Networks: A Graphical Evolutionary Game-Theoretic View

Distributed adaptive filtering has been considered as an effective approach for data processing and estimation over distributed networks. Most existing distributed adaptive filtering algorithms focus on designing 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. With the proposed formulation, the nodes in the network are regarded as players and the local combiner 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 this framework, as examples, we further propose two error-aware adaptive filtering algorithms. Moreover, we use graphical evolutionary game theory to analyze the information diffusion process over the adaptive networks and evolutionarily stable strategy of the system. Finally, simulation results are shown to verify the effectiveness of our analysis and proposed methods.Comment: Accepted by IEEE Transactions on Signal Processin

Progressive image transmission over OFDM systems using multiple antennas

A Joint source-channel coding (JSCC) scheme for SPIHT coded image transmission over OFDM systems with spatial diversity is proposed where no feedback channel is available. By using diversity techniques, the fading effects can be dramatically decreased and we show that subchannels in OFDM systems are indeed flat Rayleigh fading channels and approach Gaussian noisy channels when the diversity gain gets large, as a result, the system performance can be improved. The simulation results are presented with different number of antennas and different multipath delay and Doppler spread