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Issue Date: 2017
Title: Wireless transmission with energy harvesting and storage
Authors: Amirnavaei, Fatemeh
Publisher : UOIT
Degree : Doctor of Philosophy (PhD)
Department : Electrical and Computer Engineering
Supervisor : Dong, Min
Keywords: Energy harvesting
Online policy
Stochastic optimization
Abstract: In this dissertation, online power control strategies are proposed for wireless communication systems equipped with energy harvesting devices and finite-capacity batteries. The methods are proposed for the unbounded fading environment. Due to the time-dependent and random behavior of the energy arrival and fading, this dissertation focuses on the stochastic optimization problem to maximize the long-term average transmission rate. Leveraging the Lyapunov technique, online algorithms are designed based on the current battery energy level and fading condition. The performance gaps to the optimal scenarios are mathematically derived. The proposed algorithms do not require any statistical information (of the energy arrival and fading) and have a novel behavior of conservative energy harvesting and opportunistic transmission. The algorithms are designed for point-to-point, and relay networks. For a point-to-point channel, a three-stage closed-form online power control policy is proposed. The proposed algorithm has an opportunistic behavior based on the energy arrival and channel fading. The proposed methodology is shown to be applicable for multiantenna beamforming scenarios including MISO, SIMO, and MIMO. The analytical performance gap to the optimal solution is presented. The simulations are compared with other online algorithms in the literature and provides a superior performance. A joint online power control strategy is designed for a two-hop amplify and forward (AF) network. Both transmitter and relay are equipped with finite capacity batteries and energy harvesters. The proposed algorithm is a joint closed-form scheme that has an unique behavior in terms of the channel fading and energy arrival of both hops. Analysis is provided to illustrate the performance gap of the proposed algorithm to the optimal solution. The simulation results show that the performance significantly higher than that of existing online algorithms.
Appears in Collections:Electronic Theses and Dissertations (Public)
Faculty of Engineering and Applied Science - Doctoral Dissertations

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