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.

中文翻译：

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基于传感器对虚拟 MIMO-WBAN 中能量和信息传输的重要性和公平性的总和吞吐量最大化

在本文中，我们研究了基于传感器在虚拟多输入多输出 (MIMO) 无线体域网 (WBAN) 中的重要性和公平性的两种分类方法，其中具有多天线的接入点 (AP) 为通过广播射频 (RF) 对放置在人体不同部位的传感器进行分类。为了更好地表征射频广播信道，我们使用路径损耗模型并通过对数正态分布对链路衰落进行建模。对于两种分类传感器，我们分别在无线功率传输（WPT）阶段采用最大比传输（MRT）波束成形，在无线信息传输（WIT）阶段采用迫零（ZF）解码。由于该目标函数是一个非凸优化问题，我们将其转换为凸函数并通过 CVX 工具求解。最后，