Demand aware transmission power cost optimization based on game theory and distributed learning algorithm for wireless body area network.

This paper studied the issue of communication transmission power cost optimization for the personal body base station in wireless body area network (WBAN). With the limited energy capacity, the energy cost for the data exchange between the personal body base station and the medical surveillance network should be controlled. At the same time, the users' demand should also be guaranteed. The transmission AI choosing distributed optimization model is established, by taking the transmission power consumption as the optimization goal. In this model, the user's data transmission requirements, wireless environment, location of the AI, and other users 'choices are comprehensively analyzed. To achieve the optimal users' AI choosing result for the transmission power cost optimization, the AI choosing game model for transmission power cost of WBAN is constructed, and the game is proved to be an accurate potential game. A transmission power cost optimization AI choosing distributed decision-making algorithm is designed, and the convergence of the algorithm is proved. Experiment analysis verifies the theoretical analysis of the proposed game model and learning algorithm, and show that the proposed algorithm can effectively optimize the AI choosing results of the WBAN to reduce the energy cost. [ABSTRACT FROM AUTHOR]