  Wireless systems comprised of rechargeable nodes have a significantly
prolonged lifetime and are sustainable. A distinct characteristic of these
systems is the fact that the nodes can harvest energy throughout the duration
in which communication takes place. As such, transmission policies of the nodes
need to adapt to these harvested energy arrivals. In this paper, we consider
optimization of point-to-point data transmission with an energy harvesting
transmitter which has a limited battery capacity, communicating in a wireless
fading channel. We consider two objectives: maximizing the throughput by a
deadline, and minimizing the transmission completion time of the communication
session. We optimize these objectives by controlling the time sequence of
transmit powers subject to energy storage capacity and causality constraints.
We, first, study optimal offline policies. We introduce a directional
water-filling algorithm which provides a simple and concise interpretation of
the necessary optimality conditions. We show the optimality of an adaptive
directional water-filling algorithm for the throughput maximization problem. We
solve the transmission completion time minimization problem by utilizing its
equivalence to its throughput maximization counterpart. Next, we consider
online policies. We use stochastic dynamic programming to solve for the optimal
online policy that maximizes the average number of bits delivered by a deadline
under stochastic fading and energy arrival processes with causal channel state
feedback. We also propose near-optimal policies with reduced complexity, and
numerically study their performances along with the performances of the offline
and online optimal policies under various different configurations.
