Abstract
In adaptive diffusion networks, one of the main challenges is the large volume of data exchange among nodes needed to arrive at a collective decision. In this study, a new model for adaptive diffusion networks is proposed which offers a tradeoff between the mean-square error performance of the system and the volume of data exchanged among network nodes while preserving the network convergence rate. Study of the mean-square stability of the network under the proposed algorithms is provided. Also, a study of the mean-error dynamic behaviour of the network is carried out. A closed-form expression for the overall network steady-state means-square error is derived and verified against simulated data. The proposed algorithm is applied to a cellular network location estimation problem, and delivers good performance even under 75% reduction in data exchange volume.
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