Beam Illumination Pattern Design in Satellite Networks: Learning and Optimization for Efficient Beam Hopping

Abstract

Beam hopping (BH) is considered to provide a high level of flexibility to manage irregular and time-varying traffic requests in future multi-beam satellite systems. In BH optimization, adopting conventional iterative heuristics may have their own limitations in providing timely solutions, and directly using data-driven technique to approximate optimization variables may lead to constraint violation and degraded performance. In this paper, we explore a combined learning-and-optimization (L&O) approach to provide an efficient, feasible, and near-optimal solution. The investigations are from the following aspects: 1) Integration of BH optimization and learning techniques; 2) Features to be learned in BH design; 3) How to address the feasibility issue incurred by machine learning. We provide numerical results and analysis to show that the learning component in L&O significantly accelerates the procedure of identifying promising BH patterns, resulting in reduced computing time from seconds/minutes to milliseconds level. The identified learning feature enables high accuracy in predictions. In addition, the optimization component in L&O guarantees the solution's feasibility and improves the overall performance with around 5% gap to the optimum. INDEX TERMS Beam hopping, machine learning, neural network, optimization, satellite communications.

Precoded Cluster Hopping for Multibeam GEO Satellite Communication Systems

“…Herein, we expand Kibria et al (2019) with more technical details, expanding the numerical results. 2) Illumination pattern design: the illumination pattern design for conventional BH systems has been studied in Alegre-Godoy et al (2012), Angeletti et al (2012), Anzalchi et al (2010), Cocco et al (2018), and Lei et al (2020). While Alegre-Godoy et al (2012), and Anzalchi et al (2010) focused on heuristic iterative suboptimal algorithms, Angeletti et al (2012) and Cocco et al (2018) considered genetic and simulated annealing algorithms, respectively, targeting global optimal solutions at the expenses of increased computational complexity.…”

“…While Alegre-Godoy et al (2012), and Anzalchi et al (2010) focused on heuristic iterative suboptimal algorithms, Angeletti et al (2012) and Cocco et al (2018) considered genetic and simulated annealing algorithms, respectively, targeting global optimal solutions at the expenses of increased computational complexity. Finally, Lei et al (2020) proposed to integrate deep learning into the optimization procedure in order to accelerate the optimization procedure. Herein, we propose an illumination pattern design for CH (and therefore considering precoding the corresponding clusters) under a fair beam demand satisfaction objective.…”

Optimization Method of Dynamic Beam Position for LEO Beam-Hopping Satellite Communication Systems

“…In [21], Han et al focused on the delay fairness of each cell in the BH system, and a delay fairnessoriented BH algorithm was proposed. In [22]- [24], BH was combined with deep learning or deep reinforcement learning, which provides a novel optimization method to obtain the BH transmission scheme.…”

Precoding for Satellite Communications: Why, How and What Next?

“…The benefits of precoding techniques over broadband high-throughput satellite systems with some kind of flexibility built into them has so far received limited attention from the research community. Flexibility can be implemented mainly in two forms [23]: (i) flexible allocation of bandwidth [24]; or (ii) time flexibility (beam hopping) [25]. In [24], the limits of a frequency-flexible GEO satellite system without precoding capabilities are explored in terms of achievable user demand satisfaction rate.…”