Resource Allocation Under Uncertainty in LEO Satellite Constellation Networks

March 24, 2025·
Madyan Alsenwi
,
Eva Lagunas
,
Jorge Querol
,
Yan Kyaw Tun
,
Symeon Chatzinotas
· 1 min read
DOI
Abstract
Low Earth Orbit (LEO) satellite constellations consist of numerous satellites orbiting at different altitudes to serve diverse terrestrial users. Efficient resource management in such dynamic and large-scale networks presents a significant challenge. This paper studies Resource Blocks (RBs) and transmit power allocation at each LEO satellite, aiming at enhancing network performance while meeting Quality of Service (QoS) requirements. A stochastic optimization problem is formulated where the required QoS by each user is expressed as a chance constraint of the minimum data rate requirements considering network dynamics and uncertainties in channel conditions and traffic demands. The Conditional Value at Risk (CVaR) is employed to reformulate the chance constraint into a convex form and achieve a robust solution. Then, an alternating optimization approach is applied to solve the optimization problem. Through simulations using real data from the Starlink constellation, we demonstrate the efficacy of the proposed approach in improving network data rates while maintaining the required QoS levels.
Type
Publication
IEEE Wireless Communications and Networking Conference
publication

Abstract & Overview

Low Earth Orbit (LEO) satellite constellations consist of numerous satellites orbiting at different altitudes to serve diverse terrestrial users. Efficient resource management in such dynamic and large-scale networks presents a significant challenge. This paper studies Resource Blocks (RBs) and transmit power allocation at each LEO satellite, aiming at enhancing network performance while meeting Quality of Service (QoS) requirements. A stochastic optimization problem is formulated where the required QoS by each user is expressed as a chance constraint of the minimum data rate requirements considering network dynamics and uncertainties in channel conditions and traffic demands. The Conditional Value at Risk (CVaR) is employed to reformulate the chance constraint into a convex form and achieve a robust solution. Then, an alternating optimization approach is applied to solve the optimization problem. Through simulations using real data from the Starlink constellation, we demonstrate the efficacy of the proposed approach in improving network data rates while maintaining the required QoS levels.

Publication Metadata

  • Authors: Madyan Alsenwi, Eva Lagunas, Jorge Querol, Yan Kyaw Tun, Symeon Chatzinotas
  • Venue: IEEE Wireless Communications and Networking Conference (2025)
  • Research Areas: Satellite Communications, AI & Neuromorphic SatCom
  • DOI: 10.1109/wcnc61545.2025.10978342
Satellite Communications AI & Neuromorphic SatCom