Novel Combination Policy for Diffusion Adaptive Networks.

Diffusion adaptive networks are received attractive applications in various fields such as wireless communications. Selections of combination policies greatly influence the performance of diffusion adaptive networks. Many diffusion combination policies have been developed for the diffusion adaptive networks. However, these methods are focused either on steady-state mean square performance or on convergence speed. This paper proposes an effective combination policy, which is named as relative-deviation combination policy and uses the Euclidean norm of instantaneous deviation between the intermediate estimation vector of alone agent and the fused estimation weight to determine the combination weights of each neighbor. Computer simulations verify that the proposed combination policy outperforms the existing combination rules either in steady-state error or in convergence rate under various signal-to-noise ratio (SNR) environments.