  The evolving landscape of the Internet of Things (IoT) has given rise to a
pressing need for an efficient communication scheme. As the IoT user ecosystem
continues to expand, traditional communication protocols grapple with
substantial challenges in meeting its burgeoning demands, including energy
consumption, scalability, data management, and interference. In response to
this, the integration of wireless power transfer and data transmission has
emerged as a promising solution. This paper considers an energy harvesting
(EH)-oriented data transmission scheme, where a set of users are charged by
their own multi-antenna power beacon (PB) and subsequently transmits data to a
base station (BS) using an irregular slotted aloha (IRSA) channel access
protocol. We propose a closed-form expression to model energy consumption for
the present scheme, employing average channel state information (A-CSI)
beamforming in the wireless power channel. Subsequently, we employ the
reinforcement learning (RL) methodology, wherein every user functions as an
agent tasked with the goal of uncovering their most effective strategy for
replicating transmissions. This strategy is devised while factoring in their
energy constraints and the maximum number of packets they need to transmit. Our
results underscore the viability of this solution, particularly when the PB can
be strategically positioned to ensure a strong line-of-sight connection with
the user, highlighting the potential benefits of optimal deployment.
