Optimum Power Allocation Based on Channel Conditions in Optical Satellite Downlinks

Kapsis, Theodore T.; Panagopoulos, Athanasios D.

doi:10.1007/s11277-020-07831-z

Cited by 6 publications

(9 citation statements)

References 26 publications

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“…The optical channel for the downlink follows the LN distribution considering weak scintillation conditions. By incorporating the quantum efficiencies of the transmitter η LEO and receiver η OGS , the atmospheric transmittance T Atm , the gains of transmitter g LEO and receiver g OGS , the large-scale path loss PL, the small-scale loss due to scintillation e 2X s where X s is the log-amplitude of the optical wave and hence Gaussian (normally) distributed, the optical channel is expressed by [4,21]:…”

Section: System Modelmentioning

confidence: 99%

“…In particular, the optical noise is a zero mean, a constant variance random variable that describes the environmental optical interference. It incorporates the background radiation from the various celestial bodies, the amplified spontaneous emission (ASE) from optical preamplifiers and the electronic detection noise [4,21]. By using narrow passband optical filters and small field-of-view telescopes the receiver can eliminate the ambient background radiation and ASE, while the thermal and shot noises can be considered as additive white Gaussian noise [2].…”

Section: System Modelmentioning

confidence: 99%

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Power Allocation for Reliable and Energy-Efficient Optical LEO-to-Ground Downlinks with Hybrid ARQ Schemes

Kapsis

Panagopoulos

2022

Photonics

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Satellites in low earth orbit (LEO) are currently being deployed for numerous communication, positioning, space and Earth-imaging missions. To provide higher data rates in direct-to-user links and earth observation downlinks, the free-space optics technology can be employed for LEO-to-ground downlinks. Moreover, the hybrid automatic repeat request (HARQ) can be adopted since the propagation latency is low for LEO satellites. In this work, a power allocation methodology is proposed for optical LEO-to-ground downlinks under weak turbulence employing HARQ retransmission schemes. Specifically, the average power consumption is minimized given a maximum transmitted power constraint and a target outage probability threshold to ensure energy efficiency and reliability, respectively. The optimization problem is formulated as a constrained nonlinear programming problem and solved for Type I HARQ, chase combining (CC) and incremental redundancy (IR) schemes. The solutions are derived numerically via iterative algorithms, namely interior-point (IP) and sequential quadratic programming (SQP), and validated through an exhaustive (brute-force) search. The numerical simulations provide insight into the performance of the retransmission schemes regarding average power. More specifically, Type I HARQ has the worst output, CC has a moderate one, and IR exhibits the best performance. Finally, the IP algorithm is a slower but more accurate solver, and SQP is faster but slightly less accurate.

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Section: System Modelmentioning

confidence: 99%

Section: System Modelmentioning

confidence: 99%

Power Allocation for Reliable and Energy-Efficient Optical LEO-to-Ground Downlinks with Hybrid ARQ Schemes

Kapsis

Panagopoulos

2022

Photonics

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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%

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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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Integration of 5G technologies for enhanced performance in optical network communication with channel allocation and reallocation

Parveen Banu,

Jothilakshmi,

Radha Rammohan

et al. 2024

Opt Quant Electron

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Optimum Power Allocation Based on Channel Conditions in Optical Satellite Downlinks

Cited by 6 publications

References 26 publications

Power Allocation for Reliable and Energy-Efficient Optical LEO-to-Ground Downlinks with Hybrid ARQ Schemes

Power Allocation for Reliable and Energy-Efficient Optical LEO-to-Ground Downlinks with Hybrid ARQ Schemes

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

Integration of 5G technologies for enhanced performance in optical network communication with channel allocation and reallocation

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