Optimal User Scheduling in Energy Harvesting Wireless Networks

TL;DR

This paper develops optimal scheduling policies for energy harvesting wireless networks to maximize sum-rate and fairness, considering perfect and imperfect channel information, and demonstrates their superiority over existing myopic policies.

Contribution

It formulates and solves a non-convex sum-rate maximization problem using convex relaxation and Benders decomposition, and introduces robust policies for imperfect CSI.

Findings

Optimal policies outperform myopic strategies in achievable rates.

Robust scheduling effectively handles channel estimation errors.

Fairness can be improved by solving the minimum-rate maximization problem.

Abstract

We consider a wireless network where multiple energy harvesting transmitters communicate with the common receiver in a time sharing manner. In each slot, a transmitter can either harvest energy or send its data to the receiver. Given a time deadline, the goal is to maximize the sum-rate of transmitters under random energy arrivals with both perfect and imperfect channel state information (CSI) at the receiver. The original sumrate maximization (SRM) problem is a non-convex mixed integer non-linear program (MINLP). To obtain the optimal scheduling policy, we first reduce the original optimization problem to a convex MINLP and solve it using the generalized Benders decomposition algorithm. We observe that the SRM problem results in an unfair rate allocation among transmitters, i.e., the transmitter closer to the receiver achieves a higher rate than that by the transmitter farther from the receiver. Hence, to induce fairness among transmitters, we consider the minimumrate maximization (MRM) problem. For the bounded channel estimation error, we obtain a robust scheduling policy by solving the worst-case SRM and MRM problems. Finally, we compare the proposed policies with myopic policies studied in the literature and show that the former outperform the latter in terms of achievable rates.