Sum-Throughput Maximization Based on the Significance and Fairness of Sensors for Energy and Information Transfer in Virtual MIMO-WBAN

In this paper, we investigate two classification methods based on the significance and fairness of sensors in virtual multi-input multi-output (MIMO) wireless body area network (WBAN), where the access point (AP) with multi-antenna provides energy to classified sensors placed on different parts of human body by broadcasting radio frequency (RF). In order to better characterize RF broadcasting channel, we use path loss model and model the link fading by a log-normal distribution. For two kinds of classified sensors., we adopt maximal ratio transmission (MRT) beamforming during wireless power transfer (WPT) phase, and zero-forcing (ZF) decoding for wireless information transfer (WIT) phase, respectively. Since that objective function is a non-convex optimization problem, we convert it to a convex function and solve it by CVX tool. Finally, simulation results demonstrate the reliability of the optimal solution.

I. Introduction

With the rapid development of Micro Electro-Mechanical Systems (MEMS), the research process of wearable devices has greatly accelerated. Accordingly, the coverage of wireless body area network (WBAN) has been greatly expanded [1]. As a wireless network that specific to the human body, its sensor nodes can collect various physiological parameters from human, and send data to an access point (AP) [2]. Due to the limitations of micro-sensors (such as limited battery capacity), a major challenge is how to achieve sustainable work of WBAN. In [3], an energy-efficient networking technique is designed based on the concept of energy harvesting small cell networks (EH-SCNs).