This paper considers the problem of scheduling uplinks and downlinks transmissions in an Internet of Things (IoT) network that uses a mode-based time structure and Rate Splitting Multiple Access (RSMA). Further, devices employ power splitting to harvest energy and receive data simultaneously from a Hybrid Access Point (HAP). To this end, this paper outlines a Mixed Integer Linear Program (MILP) that can be employed by a HAP to optimize the following quantities over a given time horizon: (i) mode (downlink or uplink) of time slots, (ii) transmit power of each packet, (iii) power splitting ratio of devices, and (iv) decoding order in uplink slots. The MILP yields the optimal number of packet transmissions over a given planning horizon given non-causal channel state information. We also present a learning based approach to determine the mode of each time slot using causal channel state information. The results show that the learning based approach achieves 90% of the optimal number of packet transmissions, and the HAP receives 25% more packets as compared to competing approaches.

翻译：本文研究了采用模式化时间结构与速率分割多址接入的物联网网络中的上下行链路传输调度问题。该网络中，设备采用功率分割技术从混合接入点同时获取能量并接收数据。为此，本文构建了一个混合整数线性规划模型，该模型可供混合接入点在给定时间范围内优化以下变量：(i) 时隙模式（下行或上行），(ii) 各数据包的发射功率，(iii) 设备的功率分割比，以及(iv) 上行时隙的解码顺序。在给定非因果信道状态信息的条件下，该混合整数线性规划模型可输出规划周期内的最优数据包传输数量。本文还提出了一种基于学习的方法，利用因果信道状态信息确定各时隙的模式。实验结果表明，基于学习的方法可实现最优数据包传输数量的90%，且混合接入点接收的数据包数量较竞争方法提升25%。