Renewable energy scheduling for fading channels with maximum power constraint

In this paper, we develop efficient algorithm to obtain the optimal energy schedule for fading channel with energy harvesting. We assume that the side information of both the channel states and energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum transmission power in each time slot are limited. To obtain the achievable transmission rate, we formulate a convex optimization problem with O(K) constraints. Since the computational complexity of a generic convex solver is exponential in the number of constraints, it is hard to solve using a general convex solver and this paper gives an efficient energy scheduling algorithm, called the dynamic water-filling algorithm, obtaining the optimal energy schedule within a computational complexity of O(K²). Indifferent to the traditional water-filling algorithm, the water level in dynamic water-filling is not constant but changes when the battery overflows or depletes. Moreover, the numerical results show that the proposed algorithm achieves the optimal performance, providing a significant improvement from the traditional native scheduling policies.