Abstract
We consider multiple transmission links in a frequency-selective fading channel, where the transmitters are powered by renewable energy sources that provide variable amount of energy at different times. We formulate the problem of joint energy and subchannel allocation for all transmitters over a scheduling period, as a mixed integer program. Assuming that the harvested energy and subchannel gains can be predicted, we propose an algorithm to efficiently obtain the energy-subchannel allocations for all links over the scheduling period based on controlled water-filling. The proposed algorithm is shown to be asymptotically optimal when the bandwidth of the subchannel goes to zero. A causal algorithm is also proposed based on the Q-learning technique that makes use of the statistics of the energy harvesting and channel fading processes. Simulation results demonstrate that the performance of the proposed noncausal algorithm is close to the upper-bound on the optimal performance and the proposed causal algorithm outperforms various heuristic allocation policies.