Optimal Energy Allocation Policy for Wireless Networks in the Sky

TL;DR

This paper develops an optimal energy allocation policy for balloon-based wireless networks, using a Markov decision process and a simulation-based learning algorithm to maximize network performance and provider revenue.

Contribution

It introduces a novel stochastic optimization framework and a learning algorithm for energy management in energy-harvesting balloon networks.

Findings

The proposed algorithm converges efficiently in simulations.

Optimal policies significantly improve network performance.

Energy harvesting variability is effectively managed.

Abstract

Google's Project Loon was launched in 2013 with the aim of providing Internet access to rural and remote areas. In the Loon network, balloons travel around the Earth and bring access points to the users who cannot connect directly to the global wired Internet. The signals from the users will be transmitted through the balloon network to the base stations connected to the Internet service provider (ISP) on Earth. The process of transmitting and receiving data consume a certain amount of energy from the balloon, while the energy on balloons cannot be supplied by stable power source or by replacing batteries frequently. Instead, the balloons can harvest energy from natural energy sources, e.g., solar energy, or from radio frequency energy by equipping with appropriate circuits. However, such kinds of energy sources are often dynamic and thus how to use this energy efficiently is the main goal of this paper. In this paper, we study the optimal energy allocation problem for the balloons such that network performance is optimized and the revenue for service providers is maximized. We first formulate the stochastic optimization problem as a Markov decision process and then apply a learning algorithm based on simulation-based method to obtain optimal policies for the balloons. Numerical results obtained by extensive simulations clearly show the efficiency and convergence of the proposed learning algorithm.