As the era of Internet of things (IoT) approaches, energy harvesting over radio frequency (RF) energy, has been proposed recently as a... Show moreAs the era of Internet of things (IoT) approaches, energy harvesting over radio frequency (RF) energy, has been proposed recently as a promising solution to charge an ever increasing number of users for wireless communications. Exploiting the wireless signals in the surrounding environment coming from TV towers, Wi-Fi networks and cellular base stations (BSs), wireless devices such as wireless sensors scavenge ambient RF energy and operate self-sustainably without replacing or recharging their batteries. In this dissertation, the downlink performance of wireless networks with RF energy harvesting is investigated. We consider a large scale cellular network, where BSs and RF energy powered mobile users (MUs) are deployed as a homogeneous Poisson Point Process (HPPP) with different spatial densities. Downlink transmissions for multiple MUs associated with one BS are scheduled in a time division multiple access (TDMA) manner, which allows each MU to harvest the ambient RF energy from concurrent transmissions in other cells when it is not transmitting. Applying stochastic geometry, we develop an analytical model to investigate the energy harvesting performance of MUs and the throughput performance of the wireless network under different densities of BSs and MUs. The successful transmission probability of MUs, i.e., when an MU has charged enough energy for one transmission and the achieved signal to interference ratio is larger than a threshold, is derived. Based on the analysis, the conditions that MUs can be fully energy sustainable with RF charging are further quantified. Finally, the analytical results and the full sustainability conditions of the proposed network model have been verified by extensive simulations with Matlab. M.S. in Electrical Engineering, July 2017 Show less
