Aidos: A Hybrid Optimization Algorithm for Beam Hopping Scheduling in NGSO Mega-Constellations

2026-06-12

AI Summary

This work addresses the challenge of beam-hopping scheduling in mega-constellations with highly dynamic, large-scale service cells, where conventional methods struggle to meet the demands of long-tail traffic and stringent real-time requirements. The authors propose Aidos, an algorithm that uniquely integrates traffic-aware random-key encoding with an adaptive distribution evolution mechanism, enhanced by multi-objective metaheuristic search and a sliding-window Beta resampling strategy. This approach enables efficient computation of beam-hopping schedules at scales exceeding one thousand cells. Experimental results demonstrate that Aidos achieves online re-planning within an average of 9.3 seconds over a 300-second pass window, yielding a 79.2% improvement in throughput and a 99.45% reduction in end-to-end latency, thereby significantly overcoming the scalability and timeliness limitations of existing intelligent scheduling algorithms.

Abstract

With the rapid proliferation of non-geostationary orbit (NGSO) mega-constellations, beam hopping (BH) has become indispensable for resource scheduling in multi-satellite, multi-coverage scenarios. By dynamically adjusting spot beam power and pointing within each time slot, BH enables highly efficient spectrum utilization. A principal engineering challenge is the real-time generation of beam hopping time plans (BHTP). Traditional algorithms, such as the round-robin strategy, distribute beams evenly across all service cells in a round-robin fashion. However, real traffic follows a long-tail distribution; the most active 10% of hotspot cells generate more than 50% of the aggregate demand, making uniform allocation inadequate. To address this issue, existing frameworks adopt a genetic algorithm (GA), whose throughput is approximately 80.7% higher than the traditional baseline. Operational satellite footprints encompass more than 1,000 service cells. The GA requires 67.8 s to generate a BHTP for 1,127 cells. With a 550 km LEO satellite providing only a 300 s visibility window, multiple online recomputations are impractical. State-of-the-art algorithms, such as multi-agent deep reinforcement learning (MADRL), fail to converge once the cell count exceeds 200. To overcome these challenges, we propose a novel BH scheduling algorithm Aidos. The algorithm integrates traffic-aware random-key encoding into a multi-objective metaheuristic search, and then applies a sliding-window Beta resampling strategy during adaptive distribution evolution, to improve both the search efficiency and the solution quality of the BHTP. Experiments demonstrate that Aidos improves throughput by 79.2% and reduces latency by 99.45%. Its average computation time is 9.3 s, enabling online replanning within a 300 s satellite overpass window.

Problem

• beam hopping
• NGSO mega-constellations
• real-time scheduling
• traffic-aware resource allocation
• large-scale optimization

Innovation

• beam hopping
• hybrid optimization
• traffic-aware scheduling
• metaheuristic search
• sliding-window resampling