  We investigate an energy-harvesting wireless sensor transmitting
latency-sensitive data over a fading channel. The sensor injects captured data
packets into its transmission queue and relies on ambient energy harvested from
the environment to transmit them. We aim to find the optimal scheduling policy
that decides whether or not to transmit the queue's head-of-line packet at each
transmission opportunity such that the expected packet queuing delay is
minimized given the available harvested energy. No prior knowledge of the
stochastic processes that govern the channel, captured data, or harvested
energy dynamics are assumed, thereby necessitating the use of online learning
to optimize the scheduling policy. We formulate this scheduling problem as a
Markov decision process (MDP) and analyze the structural properties of its
optimal value function. In particular, we show that it is non-decreasing and
has increasing differences in the queue backlog and that it is non-increasing
and has increasing differences in the battery state. We exploit this structure
to formulate a novel accelerated reinforcement learning (RL) algorithm to solve
the scheduling problem online at a much faster learning rate, while limiting
the induced computational complexity. Our experiments demonstrate that the
proposed algorithm closely approximates the performance of an optimal offline
solution that requires a priori knowledge of the channel, captured data, and
harvested energy dynamics. Simultaneously, by leveraging the value function's
structure, our approach achieves competitive performance relative to a
state-of-the-art RL algorithm, at potentially orders of magnitude lower
complexity. Finally, considerable performance gains are demonstrated over the
well-known and widely used Q-learning algorithm.
