Resource Allocation for Energy Harvesting Communication Systems over Fading Channels

Abstract: Energy Harvesting (EH) is an emerging communications paradigm to defeat the limitation of network longevity by recharging the nodes by harvesting energy from the environment. The Energy Harvesting Network (EHN) requires a stable and efficient power control scheme like other conventional communication systems. It is more complicated than conventional communication networks, in that it should not only consider the quality of service requirements of the network but also adapt to the randomness of the energy arrival. In this thesis, several optimal offline and online resource allocation strategies for point-to-point and two-hop EH communication networks over wireless fading channels are investigated. As a first step, the RGWF (Recursive Geometric Water-filling) algorithm is introduced, which provides an optimal offline transmission policy for a point-to-point EH communication system. Next, a network composed of a source, a relay, and a destination, where the source is an EH node is considered. Joint time scheduling and power allocation problems are formulated to maximize the network throughput by considering conventional and bufferaided link adaptive relaying protocols. Based on the modified RGWF algorithm, the joint power allocation and transmission time scheduling problem are decoupled, and efficient offline schemes are proposed for a two-hop wireless network for delay-tolerant and delay sensitive applications. In the second part, the aim is to obtain the optimal transmission policy that maximizes the average total throughput of a point-to-point EH communication system with low and high data arrival rate in an online manner. The solution is obtained using dynamic programming by casting the proposed problem as a semi-Markov decision process (SMDP). In a delay-tolerant approach with high data rate, a cross-layer adaptation is considered, where the proposed policy chooses modulation constellation for EH networks dynamically, depending on battery state, data buffer state in addition to channel state. The proposed SMDP-based dynamic programming approach has proven to be dynamically adaptive to the change of the channel and/or buffer states that optimally satisfy the BER requirements at the physical layer, and the overflow requirements at the data-link layer.