A Comparison of artificial intelligence algorithms for dynamic power allocation in flexible high throughput satellites

Abstract

The Dynamic Resource Management (DRM) problem in the context of multibeam satellite communications is becoming more relevant than ever. The future landscape of the industry will be defined by a substantial increase in demand alongside the introduction of digital and highly flexible payloads able to operate and reconfigure hundreds or even thousands of beams in real time. This increase in complexity and dimensionality puts the spotlight on new resource allocation strategies that use autonomous algorithms at the core of their decision-making systems. These algorithms must be able to find optimal resource allocations in real or near-real time. Traditional optimization approaches no longer meet all these DRM requirements and the research community is studying the application of Artificial Intelligence (AI) algorithms to the problem as a potential alternative that satisfies the operational constraints.

Although multiple AI approaches have been proposed in the recent years, most of the analyses have been conducted under assumptions that do not entirely reflect the new operation scenarios' requirements, such as near-real time performance or high-dimensionality. Furthermore, little work has been done in thoroughly comparing the performance of different algorithms and characterizing them. This Thesis considers the Dynamic Power Allocation problem, a DRM subproblem, as a use case and compares nine different AI algorithms under the same near-real time operational assumptions, using the same satellite and link budget models, and four different demand datasets. The study focuses on Genetic Algorithms (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), Deep Reinforcement Learning (DRL), and hybrid approaches, including a novel DRL-GA hybrid. The comparison considers the following characteristics: time convergence, continuous operability, scalability, and robustness.

After evaluating the algorithms' performance on the different test scenarios, three algorithms are identified as potential candidates to be used during real satellite operations. The novel DRL-GA implementation shows the best overall performance, being also the most robust. When the update frequency is in the order of seconds, DRL is identified as the best algorithm, since it is the fastest. Finally, when the online data substantially diverges from the training dataset of the DRL algorithm, both DRL and DRL-GA hybrid might not perform adequately and an individual GA might be the best option instead.