The paper presents a novel optimization approach for resource allocation in non-geostationary orbit (NGSO) satellite constellations employing beam-hopping (BH) payloads for large-scale broadband services. Current literature has proposed several optimization strategies to deal with the intricacies of BH applied to large satellite constellations. However, most works limit their study to only sub-portions of the complete problem, often fixing certain variables, such as frequency or power, or disregarding specific objectives, such as user fairness or excess of allocated resources. To address this gap, this work introduces a novel mixed-integer linear programming (MILP) formulation that integrates all available degrees of freedom, namely, power, frequency, and beam illumination, while optimizing for performance, quality of service and fairness through three objectives: unserved capacity (UC), extra-served capacity (EC), and time-to-serve (TTS). To reduce the computational complexity, an alternative time-split MILP is proposed by partitioning the problem into smaller time-based subproblems. The time-split MILP maintains the performance of the full MILP formulation in larger user scenarios and balances execution time with superior computational performance. Simulations demonstrate that the time-split MILP method surpasses other approaches, including deterministic (DB) and meta-heuristic (GA) solutions. It achieves up to a 6% reduction in UC, maintains EC levels below those of DB and GA in low-user scenarios, and nearly halves the TTS, highlighting its efficiency in resource allocation and service optimization.

With the reduced distance between satellites in modern megaconstellations, the potential for self-interference has emerged as a critical challenge that demands strategic solutions from satellite operators. The goal of this paper is to propose a cooperative framework that combines the Satellite Routing (i.e., mapping of beams to satellites) and Frequency Assignment (i.e., mapping of frequency spectrum to beams) strategies to mitigate self-interference both within and between satellites. This approach stands in contrast to current practices found in the literature, which address each problem independently and solely focus on intra-satellite interference. This study presents a novel methodology for addressing the Satellite Routing problem, specifically tailored for modern constellations to maximize capacity while effectively mitigating self-interference through the use of Integer Optimization. By combining this method with established Frequency Assignment techniques, the results demonstrate an increase in throughput of up to 138% for constellations such as SpaceX Starlink. Notably, the study reveals that relying on individual approaches to tackle interference may lead to undesired outcomes, underscoring the advantages of a cooperative framework. Through simulations, the study highlights the practicality and applicability of the proposed method under realistic operational conditions.

Upcoming large satellite constellations and the advent of tighter steerable beams will offer unprecedented flexibility. Consequently, this will require resource management strategies to be operated in high-dimensional and dynamic environments, as existing satellite operators are unaccustomed to operational flexibility and automation. Frequency assignment policies have the potential to drive constellations’ performance in this new context, but are no exception to scalability and fast operation requirements. Most of existing frequency assignment methods fail to fulfill these requirements, or are unable to meet them without falling short on efficiency. In this paper we propose a new frequency assignment method that prioritizes operational requirements. We present an algorithm based on Integer Linear Programming that fully defines a frequency plan while respecting key system constraints such as handovers, interference, and gateway dimensioning. We can encode goals such as bandwidth maximization or power reduction and optimize plans according to such objectives. In our experiments on systems with 20 to 5,000 beams, we find this method allocates at least 100% more bandwidth and reduces power consumption by 40% compared to previous benchmarks. To ensure scalability, we also introduce an iterative approach of the formulation, which achieves substantial gains in runtime with minimal loss of performance.