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

2025-12-09

AI Summary

Real-time illumination map generation for high-throughput satellite (HTS) beam hopping with large-scale cells (>300) remains intractable—conventional optimization methods (e.g., genetic algorithms) incur excessive latency (>300 s for 37 cells), while advanced learning-based approaches (e.g., multi-agent deep reinforcement learning) suffer from poor convergence beyond 40 cells. Method: This paper proposes a hybrid computational framework integrating Monte Carlo Tree Search–based Beam Hopping (MCTS-BH) and Greedy Beam Hopping (G-BH) in a synergistic mechanism, augmented by sliding-window scheduling, dynamic pruning, and multi-level parallelization. Contribution/Results: Experiments demonstrate that the framework reduces per-instance computation time to 12 seconds for 37 cells—a 25× speedup—while improving throughput by 98.76%. It enables millisecond-level responsiveness and, for the first time, achieves high-accuracy, real-time illumination map generation at scale exceeding 300 cells.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Hardware-aware ML

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

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.

Problem

• Computes illumination patterns for satellite beam hopping efficiently
• Reduces computation time for large-scale cell coverage scenarios
• Ensures real-time performance with hybrid algorithmic framework

Innovation

• Hybrid framework combining MCTS-BH and G-BH algorithms
• Monte Carlo Tree Search with sliding window and pruning
• Real-time provisional solution via Greedy Beam Hopping