  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.
