Abstract
Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for self-powered wireless networks. This paper jointly characterizes the harvested energy and the packet transmission success probability in grant-free uplink IoT networks energized via harvesting downlink energy. To do that, a joint queueing theory and stochastic geometry model is exploited and a spatiotemporal analytical model is developed accordingly. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a two-dimensional discrete-time Markov chain (DTMC) to track the time evolution of the joint states of the scavenged energy and the data buffer. Consequently, the adopted queueing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average waiting time, and the average buffer size which offer valuable insights for network design.