Optimized Deployment of Autonomous Drones to Improve User Experience in Cellular Networks

TL;DR

This paper proposes optimized algorithms for deploying autonomous drones in cellular networks to enhance user experience, considering real-world constraints like drone battery life and collision avoidance, supported by extensive simulations.

Contribution

It introduces novel greedy algorithms for drone deployment problems, including battery constraints, and proves their NP-hardness, filling a gap in existing research.

Findings

Algorithms effectively maximize user coverage

Battery constraints significantly impact deployment strategies

Simulations confirm the algorithms' efficiency and practicality

Abstract

Modern wireless traffic demand pushes Internet Service Providers to develop effective strategies to improve user experience. Since deploying dense Base Stations (BSs) is not cost efficient, an alternative is to deploy autonomous drones to supplement existing BSs. A street graph is adopted to represent the area of interest. The outdoor User Equipments (UEs) to be served locate near streets and the 2D projections of drones are restricted to streets to avoid collision with buildings. We build up a UE density function based on a real dataset, reflecting the traffic in the area. We study four problems: where to deploy single drone to cover maximum UEs, where to deploy $k$ drones cover maximum UEs subject to an inner drone distance constraint, where to deploy $k$ drones cover maximum UEs subject to inner drone distance constraint and drones' battery constraints, and the minimum drones to cover a given percentage of UEs subject to inner drone distance constraint. We prove that the latter three problems are NP-hard and propose greedy algorithms with theoretical analysis. To our best knowledge, this is the first paper to consider the battery constraints for drone deployments. Extensive simulations have been conducted to verify the effectiveness of our approaches.