Energy Harvesting Networks with General Utility Functions: Near Optimal Online Policies

We consider online scheduling policies for single-user energy harvesting communication systems, where the goal is to characterize online policies that maximize the long term average utility, for some general concave and monotonically increasing utility function. In our setting, the transmitter relies on energy harvested from nature to send its messages to the receiver, and is equipped with a finite-sized battery to store its energy. Energy packets are independent and identically distributed (i.i.d.) over time slots, and are revealed causally to the transmitter. Only the average arrival rate is known a priori. We first characterize the optimal solution for the case of Bernoulli arrivals. Then, for general i.i.d. arrivals, we first show that fixed fraction policies [Shaviv-Ozgur] are within a constant multiplicative gap from the optimal solution for all energy arrivals and battery sizes. We then derive a set of sufficient conditions on the utility function to guarantee that fixed fraction policies are within a constant additive gap as well from the optimal solution.Comment: To appear in the 2017 IEEE International Symposium on Information Theory. arXiv admin note: text overlap with arXiv:1705.1030

Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks

Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings

Optimal Energy Management for Energy Harvesting Transmitter and Receiver with Helper

We study energy harvesting (EH) transmitter and receiver, where the receiver decodes data using the harvested energy from the nature and from an independent EH node, named helper. Helper cooperates with the receiver by transferring its harvested energy to the receiver over an orthogonal fading channel. We study an offline optimal power management policy to maximize the reliable information rate. The harvested energy in all three nodes are assumed to be known. We consider four different scenarios; First, for the case that both transmitter and the receiver have batteries, we show that the optimal policy is transferring the helper harvested energy to the receiver, immediately. Next, for the case of non-battery receiver and full power transmitter, we model a virtual EH receiver with minimum energy constraint to achieve an optimal policy. Then, we consider a non-battery EH receiver and EH transmitter with battery. Finally, we derive optimal power management wherein neither the transmitter nor the receiver have batteries. We propose three iterative algorithms to compute optimal energy management policies. Numerical results are presented to corroborate the advantage of employing the helper.Comment: It is a conference paper with 5 pages and one figure, submitted to ISITA201