Power Scheduling for Energy Harvesting Wireless Communications With Battery Capacity Constraint

Power scheduling is an important issue for energy harvesting systems. In this work, we study the power control policy for minimizing the weighted sum of the outage probabilities under a set of predetermined transmission rates over a finite horizon. This problem is challenging in that the objective function is non-convex. To make the analysis tractable, we apply the approximation at high signal-to-noise ratios and obtain a near-optimal offline solution. In the case of infinite battery capacity, we demonstrate that the allocated power has a piecewise structure, i.e., each power scheduling cycle should be divided into disjoint segments and the normalized power should remain constant within each segment. An iterative algorithm is developed to obtain the power solution. In the case of finite battery capacity, we show that the piecewise structure still holds true, and we develop a divide-and-conquer algorithm to recursively solve the power allocation problem. Finally, we obtain a simple online power control policy that is fairly robust to prediction errors of the harvested energy. Simulations demonstrate that the proposed power solution has better performance than other strategies such as best-effort, fixed-ratio and random allocation.