In this paper, we develop optimal energy scheduling algorithms for optical Poisson channels with energy harvesting devices. The objective is to maximize the channel sum-rate, assuming that the side information of energy harvesting states for K time slots is known a priori, and the battery capacity and the maximum energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O(K) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver is exponential in the number of constraints. This paper gives an efficient energy scheduling algorithm that has a computational complexity of O(K2). The proposed algorithm is also shown to be optimal. The proposed energy schedule is piece-wise constant, which changes when the battery overflows or depletes. Numerical results depict significant improvement of the optimal strategy over benchmark strategies.

In this paper, we develop optimal energy-bandwidth allocation algorithms in fading channels for multiple energy harvesting transmitters. We first assume that the side information of both the channel states and the energy harvesting states is known for K time slots a priori, and the battery capacity and the maximum transmission power in each time slot are bounded. The network consists of N transmitter-receiver pairs and the objective is to maximize the sum-rate of all communication links over the K time slots by assigning the transmission power and bandwidth for each transmitter in each slot. The problem is formulated as a convex optimization problem with O(N K) constraints, where N is the number of the receivers, making it hard to solve with a generic convex solver. An iterative algorithm is proposed based on efficiently solving two subproblems in each iteration, that has an overall complexity of O(N K2). The convergence and the optimality of this algorithm are also shown. Moreover, a heuristic algorithm is also proposed for energy-bandwidth allocation based on causal information of channel and energy harvesting states. Simulation results show that the proposed causal and noncausal algorithms can make efficient use of the harvested energy and the available bandwidth. And they achieve significantly higher rates than some heuristic policies for energy and bandwidth allocation.

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.

In this paper, we consider the energy-bandwidth allocation for a network with multiple orthogonal broadcast channels, where each transmitter communicates with multiple receivers orthogonally. We assume that the harvested energy and channel gain of each transmitter can be predicted for K slots a priori. To maximize the weighted throughput of the network, we formulate an optimization problem with O(MK) constraints, where M is the number of the receivers, making it hard to solve using a generic convex solver since the computational complexity of the solver becomes impractically high when the number of constraints is large. In order to use the iterative algorithm proposed in [1] to solve the problem efficiently, we decompose the problem into the energy and bandwidth allocation subproblems and propose algorithms to solve the two corresponding subproblems, so that the optimal energy-bandwidth allocation can be obtained with an overall complexity of O(MK2).

We consider an energy harvesting network with multiple transmission links in a frequency-selective fading channel. We formulate the problem of joint energy and subchannel allocation for all transmitters over a scheduling period, as a mixed integer program. With the predictions of the harvested energy and subchannel gains, 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. Simulation results demonstrate that the performance of the proposed algorithm is close to the upper-bound on the optimal performance, which is also outperforms various heuristic allocation policies.

In this paper, we develop optimal energy scheduling algorithms for N-user fading multiple-access channels with energy harvesting to maximize the channel sum-rate, assuming 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 energy consumption in each time slot are bounded. The problem is formulated as a convex optimization problem with O (NK) constraints making it hard to solve using a general convex solver since the computational complexity of a generic convex solver becomes impractically high when the number of constraints is large. This paper gives an efficient energy scheduling algorithm, called the iterative dynamic water-filling algorithm, that has a computational complexity of O(NK2) per iteration. For the single-user case, a dynamic water-filling method is shown to be optimal. Unlike the traditional water-filling algorithm, in dynamic water-filling, the water level is not constant but changes when the battery overflows or depletes. An iterative version of the dynamic water-filling algorithm is shown to be optimal for the case of multiple users. Even though in principle the optimality is achieved under large number of iterations, in practice convergence is reached in only a few iterations. Moreover, a single iteration of the dynamic water-filling algorithm achieves a sum-rate that is within (N-1)K nats of the optimal sum-rate.

In this paper, we consider the energy-bandwidth allocation for a network with multiple broadcast channels, where the transmitters each powered by an energy harvester, access the network orthogonally on the assigned frequency band and each transmitter communicates with multiple receivers orthogonally or non-orthogonally. We assume that the energy harvesting state and channel gain of each transmitter can be predicted for K time slots a priori. To maximize the weighted throughput, we formulate an optimization problem with O(MK) constraints, where M is the total number of receivers, and optimize over the energy and bandwidth allocation variables. To solve the problem efficiently, an iterative algorithm is proposed that alternatively solves the two subproblems of energy allocation and bandwidth allocation in each iteration. We show that this algorithm converges to the optimal solution. Also, we propose efficient algorithms to solve the two subproblems, so that the optimal energy-bandwidth allocation can be obtained with an overall complexity of O(MK2), even though the problem is non-convex when the broadcast channel is non-orthogonal. Simulation results show that the proposed algorithms can make efficient use of the harvested energy and the available bandwidth, and achieve significantly better performance as compared to some heuristic policies for energy and bandwidth allocation. Moreover, it is seen that with energy-harvesting transmitters, the non-orthogonal broadcast channel offers limited gain over the orthogonal broadcast channel.

We develop optimal energy-bandwidth allocation algorithm for the energy harvesting transmitters in interference networks. We assume that both the channel gain and the harvested energy are known for K slots as a priori, and the battery capacity is finite. The problem is formulated as a convex optimization problem with O(NK) constraints, making it hard to solve efficiently with a generic convex solver. To efficiently obtain the optimal energy-bandwidth allocation for each transmitter in each time slot, an iterative algorithm is proposed based on solving two subproblems with efficient algorithms, that has an overall complexity of O(NK2). Moreover, the numerical results show that the proposed iterative algorithm achieves the optimal performance, providing a significant improvement as compared to some naive allocation policies.

We consider power allocation for an access-controlled transmitter with energy harvesting capability based on causal observations of the channel fading state. We assume that the system operates in a time-slotted fashion and the channel gain in each slot is a random variable which is independent across slots. Further, we assume that the transmitter is solely powered by a renewable energy source and the energy harvesting process can practically be predicted. With the additional access control for the transmitter and the maximum power constraint, we formulate the stochastic optimization problem of maximizing the achievable rate as a Markov decision process (MDP) with continuous state. To efficiently solve the problem, we define an approximate value function based on a piecewise linear fit in terms of the battery state. We show that with the approximate value function, the update in each iteration consists of a group of convex problems with a continuous parameter. Moreover, we derive the optimal solution to these convex problems in closed-form. Further, we propose power allocation algorithms for both the finite- and infinite-horizon cases, whose computational complexity is significantly lower than that of the standard discrete MDP method but with improved performance. Extension to the case of a general payoff function and imperfect energy prediction is also considered. Finally, simulation results demonstrate that the proposed algorithms closely approach the optimal performance.

We propose a green femto-station (FS) assignment algorithm for a hierarchical cellular network. It is assumed that the cellular network consists of one macro station, several femto stations equipped with the renewable energy source, and several battery-driven mobile stations. We also assume that the FS has limited incoming energy, and the occurrence and the duration of the mobile station's downlink transmission request is a stochastic process. To improve the service quality, i.e., reduce the probability of the FSs' service outage, a stationary FS assignment schedule is obtained for certain period by solving a combinatorial optimization problem. First we construct a Markov chain model to estimate the service outage probability, i.e., rejection probability, for given FS assignment schedule; based on the estimation model, we then propose a low-complexity local search algorithm, which balances the computation complexity and the performance, to obtain the local optimal FS assignment schedule efficiently. Simulation results are provided to demonstrate the superior performance of the proposed techniques over the traditional methods.

We solve the problem of designing an affordable optimal transmission strategy for the recently proposed system of energy-harvesting active networked tags (EnHANTs), that is adapted to the identification request and the energy harvesting dynamic. We assume that the system operates in a time-slotted fashion, so that the problem is formulated as a Markov decision process (MDP). Both a static exhaustive search method and a modified policy iteration algorithm are employed to obtain the optimal transmission policy. Simulation results are provided to demonstrate that the obtained transmission policy can considerably improve the overall system performance which takes into consideration of both the system activity-time and the communication reliability.