Problem representation of dynamic resource allocation for flexible high throughput satellities

Guerster, Markus; Luis, Juan; Crawley, Edward F.; Cameron, Bruce

doi:10.1109/aero.2019.8741398

Cited by 16 publications

(11 citation statements)

References 12 publications

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“…In the specific case of Dynamic Resource Management (DRM) in multibeam High Throughput Satellites (HTS), while traditional approaches were based on static, human-controlled policies that relied on conservative operational margins, new systems will include dynamic algorithms capable of handling the increasing dimensionality and the rapidly-changing nature of the demand [4]. Consequently, these systems will be prepared to make quick decisions on multiple parameters per beam across thousands of active spot beams.…”

Section: Motivationmentioning

confidence: 99%

Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison

Luis

Pachler

Guerster

et al. 2020

2020 IEEE Aerospace Conference

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Automating resource management strategies is a key priority in the satellite communications industry. The future landscape of the market will be changed by a substantial increase of data demand and the introduction of highly flexible communications payloads able to operate and reconfigure hundreds or even thousands of beams in orbit. This increase in dimensionality and complexity puts the spotlight on Artificial Intelligence-based dynamic algorithms to optimally make resource allocation decisions, as opposed to previous fixed policies. Although multiple approaches have been proposed in the recent years, most of the analyses have been conducted under assumptions that do not entirely reflect operation scenarios. Furthermore, little work has been done in thoroughly comparing the performance of different algorithms.In this paper we compare some of the recently proposed dynamic resource allocation algorithms under realistic operational assumptions, addressing a specific problem in which power needs to be assigned to each beam in a multibeam High Throughput Satellite (HTS). We focus on Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Deep Reinforcement Learning, and hybrid approaches. Our multibeam operation scenario uses demand data provided by a satellite operator, a full radio-frequency chain model, and a set of hardware and time constraints present during the operation of a HTS. We compare these algorithms focusing on the following characteristics: time convergence, continuous operability, scalability, and robustness. We evaluate the performance of the algorithms against different test cases and make recommendations on the approaches that are likely to work better in each context.

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Section: Motivationmentioning

confidence: 99%

Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison

Luis

Pachler

Guerster

et al. 2020

2020 IEEE Aerospace Conference

Self Cite

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“…In the recent years, the DRM problem has become a wellstudied topic, both in academia and industry. Within the DRM problem, literature often identifies four resources to allocate [3]: radio-transmitted power, radio-transmission frequency, beam pointing and beam shape. The frequency assignment problem can be further subdivided into the beam's frequency allocation and the bandwidth allocation.…”

Section: Literature Reviewmentioning

confidence: 99%

Allocating Power and Bandwidth in Multibeam Satellite Systems using Particle Swarm Optimization

Pachler

Luis

Guerster

et al. 2020

2020 IEEE Aerospace Conference

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In the recent years, communications satellites' payloads have been evolving from static subsystems to highly flexible components. Modern satellites are able to provide four orders of magnitude higher throughput than their predecessors fourty years ago, going from a few Mbps to several hundreds of Gbps. This enhancement in performance is aligned with an increasing highly-variable demand. In order to dynamically and efficiently manage the satellite's resources, an automatic tool is needed.This work presents an implementation of a new metaheuristic algorithm based on Particle Swarm Optimization (PSO) to solve the joint power and bandwidth allocation problem. We formulate this problem as a multi-objective approach that considers the different constraints of a communication satellite system. The evaluation function corresponds to a full-RF link budget model that accounts for adaptive coding and modulation techniques as well as multiple types of losses. We benchmark the algorithm using a realistic traffic model provided by a satellite communications operator and under time restrictions present in an operations environment.

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“…• Micro/real time scale: in this case the optimization focuses on real time bandwidth and time slot allocation (Al-Mosawi et al, 2012), with short-term impairments and traffic prediction (Aroumont et al, 2008). Techniques such as connection admission control (Tra, 2008), channel estimation (Cioni et al, 2004), power allocation (Guerster et al, 2019), and adaptive coding and modulation (Bischl et al, 2010) fall in this category. • Macro/system scale: in this case the optimization is done on a system level, dealing with long-term statistics of channel behaviour, link budget outputs, and traffic variations.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the Return Link Carrier Planning for a Constant Coding and Modulation Satellite Network

et al. 2021

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In this paper, we propose an approach to optimize the frequency plan and associated bandwidth allocation in the return link of a broadband satellite network, by exploring several design techniques for carrier allocation plans. Since bandwidth is a limited resource in satellite telecommunications, the minimization of bandwidth usage is a core issue that satellite communication service providers must solve, in particular for networks using a constant coding and modulation plan, which lacks the flexibility found in newer satellite communication products and can be subject to hardware constraints. This problematic led us to raise the following question: how can the long term bandwidth requirement of the network be minimized, given a set of ground terminals, of Modulations and Codings, and of discrete bandwidths. In this document we formally define the long-term carrier allocation problem and analyze current practical solutions. We subsequently investigate two other potential solutions, found to be more bandwidth-efficient: one based on heuristics and another based on integer linear programming. Finally, we look at the impact of several parameters on the performance of those three methods. Overall we observed marginal reductions in bandwidth, however significant gains were reached for networks with small return links with low committed information rate, a case in which some constant coding and modulation networks could fall. We concluded that those networks could benefit from our methods and see a significant reduction in bandwidth, and subsequently operational costs, at low implementation costs.

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Problem representation of dynamic resource allocation for flexible high throughput satellities

Cited by 16 publications

References 12 publications

Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison

Artificial Intelligence Algorithms for Power Allocation in High Throughput Satellites: A Comparison

Allocating Power and Bandwidth in Multibeam Satellite Systems using Particle Swarm Optimization

Optimization of the Return Link Carrier Planning for a Constant Coding and Modulation Satellite Network

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