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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.

Submission history

From: Kun Qiu [view email]
[v1] Fri, 12 Jun 2026 06:24:48 UTC (7,211 KB)