The QoE driven transmission optimization based on cognitive air interface match for self-organized wireless body area network

Abstract

This paper studied the optimization of air interface (AI) allocation in self-organized wireless body area network (WBAN), for the quality of experience (QoE) of wireless data transmission between patients' body base station and the medical surveillance network (MSN). To improve the spectrum efficiency, the partial overlapping channel (POC) is adopted for the cognitive AI, which could adjust its wireless channel according to the environment. In the same time, the heterogeneous QoE of patients is also taken into consideration to optimize the network. A two-layer game model and the corresponding learning algorithm have been proposed for the AIs' channel choosing and body base stations' AI choosing in a distributed way, to realize the optimized and stable match between AI and patient's transmission demand. The theoretic analysis for the equilibrium of the game and the convergence of the proposed algorithm is carried out. Simulation experiment results shows that the proposed two-layer game model and learning algorithm could effectively improve the QoE of WBAN, along with the fairness.