Solar energy paper index

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

2026-06-12 · arXiv: 2606.14151

One-line summary

A solar energy research paper on Aidos: A Hybrid Optimization Algorithm for Beam Hopping Scheduling in NGSO Mega-Constellations.

Engineering notes

Engineering notes will be added by the Power for Solar editorial team.

Chinese explanation / 中文解读

中文解读待补充:本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original 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.

5.0Engineering value
7.0Research novelty
4.0Business relevance

Links and sources

Need this topic turned into a technical roadmap?

Power for Solar can prepare a custom solar energy literature review, simulation code map, dataset map, and B2B photovoltaic technology assessment.

Request B2B research

Comments

No comments yet. Be the first to share your thoughts on this paper.
Login or register to leave a comment