Tyche: A Hybrid Computation Framework of Illumination Pattern for Satellite Beam Hopping

TL;DR

Tyche addresses real-time illumination-pattern computation for high-throughput satellite beam hopping by introducing a hybrid framework that combines a fast online Greedy Beam Hopping (G-BH) method with an offline Monte Carlo Tree Search Beam Hopping (MCTS-BH) engine. It locks in high-throughput patterns via MCTS-BH while maintaining millisecond responsiveness with G-BH and a Redis-backed cache of precomputed patterns; two optimizations—sliding-window scoring and pruning—significantly accelerate MCTS-BH without sacrificing throughput. Empirical results show up to 98.76% throughput gains over baselines and substantial reductions in computation time (up to 81.41%) in large-scale cell scenarios, enabling real-time decision-making. The work demonstrates a viable route to scalable, demand-aware beam hopping in HTS and outlines plans for extending to multi-satellite networks with inter-satellite synchronization and interference considerations.

Abstract

High-Throughput Satellites (HTS) use beam hopping to handle non-uniform and time-varying ground traffic demand. A significant technical challenge in beam hopping is the computation of effective illumination patterns. Traditional algorithms, like the genetic algorithm, require over 300 seconds to compute a single illumination pattern for just 37 cells, whereas modern HTS typically covers over 300 cells, rendering current methods impractical for real-world applications. Advanced approaches, such as multi-agent deep reinforcement learning, face convergence issues when the number of cells exceeds 40. In this paper, we introduce Tyche, a hybrid computation framework designed to address this challenge. Tyche incorporates a Monte Carlo Tree Search Beam Hopping (MCTS-BH) algorithm for computing illumination patterns and employs sliding window and pruning techniques to significantly reduce computation time. Specifically, MCTS-BH can compute one illumination pattern for 37 cells in just 12 seconds. To ensure real-time computation, we use a Greedy Beam Hopping (G-BH) algorithm, which provides a provisional solution while MCTS-BH completes its computation in the background. Our evaluation results show that MCTS-BH can increase throughput by up to 98.76%, demonstrating substantial improvements over existing solutions.

Figures (16)

• Figure 1: Comparison of HTS and HTS using beam hopping service modes. HTS using beam hopping can more flexibly schedule beam resources and select different service cells in different time slots according to demand.
• Figure 2: An example of a beam hopping system, where the NOCC calculates the BHTP and sends it to the satellite. The satellite manages the beams according to the BHTP.
• Figure 3: Time slot structure illustrating multiple superframes transmitted within a single timeslot.
• Figure 4: The overview workflow of Tyche, Tyche consists of a traffic analysis module and a computation module. The traffic analysis module is used to query the local database for the precomputed BHTP. The computation module computes the BHTP, utilizing both MCTS-BH and G-BH simultaneously. The result of G-BH is output directly, while the result of MCTS-BH is stored in the database.
• Figure 5: The database storage format for the traffic analysis module