Robust Power Allocation in Optical Satellite MIMO Links With Pointing Jitter

Kapsis, Theodore T.; Panagopoulos, Athanasios D.

doi:10.1109/lwc.2022.3151730

Cited by 3 publications

(7 citation statements)

References 23 publications

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“…Moreover, the birth of the new Cubesat technology represents another important motivation for continuing to analyze, study, and propose new channel models that are able to always boost network performance [65]. As shown in [66], the new frontier of satellite networks has progressed toward optical communications. In this work, the authors have investigated a robust power-allocation strategy for the downlink, taking into account the effects of the atmospheric impairments.…”

Section: Future Research Directionmentioning

confidence: 99%

A Comprehensive Review of Channel Modeling for Land Mobile Satellite Communications

Tropea

Rango

2022

Electronics

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As demands on the network continue to grow, it is increasingly important to upgrade the existing infrastructure in order to offer higher bandwidth and service level guarantees to users. Next generation networks (NGNs) represent a fully IP-based architecture that is able to support different technologies. In this context, the satellite networks are considered a fundamental part for future hybrid architectures. In this scenario, knowing satellite channel propagation characteristics in order to be able to design a communication system to respond to new user needs is of fundamental importance. Many papers in the literature show channel models in different satellite scenarios both for fixed and mobile applications; however, to the best of our knowledge, nobody presents an overview on different satellite models based on Markov chains. This paper wants to present a comprehensive review of the most recent developments in satellite channel communications related to mobile services and, in particular, for the land mobile satellite systems. The work presents all different types of Markov models, from single-state to multi-state models, that have been proposed in the literature from the early 1980s.

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Section: Future Research Directionmentioning

confidence: 99%

A Comprehensive Review of Channel Modeling for Land Mobile Satellite Communications

Tropea

Rango

2022

Electronics

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“…For mitigating the cloud occlusion, site diversity can be used [8]. Received irradiance scintillation is created from rapid changes in wind speed, pressure, and temperature, which in turn induce changes in the refractive index [10][11][12]. The impact of scintillation depends primarily upon the time of day, the elevation angle of the link, and the altitude of the station [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Received irradiance scintillation is created from rapid changes in wind speed, pressure, and temperature, which in turn induce changes in the refractive index [10][11][12]. The impact of scintillation depends primarily upon the time of day, the elevation angle of the link, and the altitude of the station [10][11][12]. In the daytime, at low elevation angles and low-altitude stations (denser atmosphere), turbulence is more extreme [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…The impact of scintillation depends primarily upon the time of day, the elevation angle of the link, and the altitude of the station [10][11][12]. In the daytime, at low elevation angles and low-altitude stations (denser atmosphere), turbulence is more extreme [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…In [10][11][12], the power allocation (PA) problem is investigated for optical satellite downlinks under weak turbulence and solved using a Karush-Kuhn-Tucker (KKT), waterfilling-inspired algorithm. However, the reported methodology depends on the knowledge of the system and channel model.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Power Allocation in Optical GEO Satellite Downlinks Using Model-Free Deep Learning Algorithms

Kapsis,

Lyras,

Panagopoulos

2024

Electronics

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Geostationary (GEO) satellites are employed in optical frequencies for a variety of satellite services providing wide coverage and connectivity. Multi-beam GEO high-throughput satellites offer Gbps broadband rates and, jointly with low-Earth-orbit mega-constellations, are anticipated to enable a large-scale free-space optical (FSO) network. In this paper, a power allocation methodology based on deep reinforcement learning (DRL) is proposed for optical satellite systems disregarding any channel statistics knowledge requirements. An all-FSO, multi-aperture GEO-to-ground system is considered and an ergodic capacity optimization problem for the downlink is formulated with transmitted power constraints. A power allocation algorithm was developed, aided by a deep neural network (DNN) which is fed channel state information (CSI) observations and trained in a parameterized on-policy manner through a stochastic policy gradient approach. The proposed method does not require the channels’ transition models or fading distributions. To validate and test the proposed allocation scheme, experimental measurements from the European Space Agency’s ARTEMIS optical satellite campaign were utilized. It is demonstrated that the predicted average capacity greatly exceeds other baseline heuristic algorithms while strongly converging to the supervised, unparameterized approach. The predicted average channel powers differ only by 0.1 W from the reference ones, while the baselines differ significantly more, about 0.1–0.5 W.

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Deep Reinforcement Learning Module for Power Allocation in Optical Satellite Networks

Kapsis,

Lyras,

Panagopoulos

2024

2024 Panhellenic Conference on Electronics &Amp;amp; Telecommunications (PACET)

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Robust Power Allocation in Optical Satellite MIMO Links With Pointing Jitter

Cited by 3 publications

References 23 publications

A Comprehensive Review of Channel Modeling for Land Mobile Satellite Communications

A Comprehensive Review of Channel Modeling for Land Mobile Satellite Communications

Optimal Power Allocation in Optical GEO Satellite Downlinks Using Model-Free Deep Learning Algorithms

Deep Reinforcement Learning Module for Power Allocation in Optical Satellite Networks

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