Continent-wide Efficient and Fair Downlink Resource Allocation in LEO Satellite Constellations

TL;DR

This paper introduces distributed and global optimal algorithms for fair and efficient downlink resource allocation in LEO satellite networks, balancing throughput, fairness, and handover reduction.

Contribution

It proposes novel algorithms for satellite-to-cell resource allocation that improve fairness and reduce handovers in LEO satellite constellations.

Findings

Global optimal algorithm achieves fairness index over 0.9

Both algorithms attain similar average per-user throughput

Handover reduction exceeds 70% with proper parameter tuning

Abstract

The integration of Low Earth Orbit (LEO) satellite constellations into 5G and Beyond is essential to achieve efficient global connectivity. As LEO satellites are a global infrastructure with predictable dynamics, a pre-planned fair and load-balanced allocation of the radio resources to provide efficient downlink connectivity over large areas is an achievable goal. In this paper, we propose a distributed and a global optimal algorithm for satellite-to-cell resource allocation with multiple beams. These algorithms aim to achieve a fair allocation of time-frequency resources and beams to the cells based on the number of users in connected mode (i.e., registered). Our analyses focus on evaluating the trade-offs between average per-user throughput, fairness, number of cell handovers, and computational complexity in a downlink scenario with fixed cells, where the number of users is extracted from a population map. Our results show that both algorithms achieve a similar average per-user throughput. However, the global optimal algorithm achieves a fairness index over 0.9 in all cases, which is more than twice that of the distributed algorithm. Furthermore, by correctly setting the handover cost parameter, the number of handovers can be effectively reduced by more than 70% with respect to the case where the handover cost is not considered.