Energy-Efficient Aerial Network Slicing for Computation Offloading, Data Gathering, and Content Delivery

TL;DR

This paper proposes an energy-efficient UAV-enabled network slicing framework that optimizes deployment, resource allocation, and user association to support content delivery, sensing, and computing services with minimized energy use.

Contribution

It introduces a novel optimization framework for UAV-based network slicing that jointly considers deployment, resource allocation, and user association to enhance energy efficiency.

Findings

Significant reduction in total energy consumption achieved.

Effective optimization of UAV deployment and resource allocation.

Incorporation of spatial correlation among sensing users.

Abstract

This paper introduces an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery, sensing data gathering, and mobile edge computing (MEC) services. Three tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, the sensing tenant aims to gather an adequate amount of uncorrelated data, and the MEC tenant provides computing service to its clients. An energy consumption minimization framework is considered to meet the tenants' requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, the user-UAV association, and the transmission power as well as the computing resources of each UAV. Taking into account the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A solution approach technique inherited from graph theory is presented, in which the Lagrange approach derives the transmission power and computing resource allocation expressions. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.