Assessment of spatial and temporal properties of <scp>Ka/Q</scp> band earth‐space radio channel across Europe using <scp>Alphasat Aldo Paraboni</scp> payload

Ventouras, S.; Martellucci, A.; Reeves, Rochelle; Rumi, Emal; Fontán, F.P.; Machado, Fernando; Pastoriza, V.; Rocha, Armando; Mota, Susana; Jorge, Flavio; Panagopoulos, Athanasios D.; Papafragkakis, Apostolos Z.; Kourogiorgas, Charilaos I.; Fišer, Ondřej; Pek, Viktor; Pešice, Petr; Grábner, Martin; Vilhar, Andrej; Kelmendi, Arsim; Hrovat, Andrej; Vanhoenacker‐Janvier, Danielle; Quibus, Laurent; Goussetis, George; Costouri, Alexios; Nessel, James A.

doi:10.1002/sat.1313

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…However, the majority of these models are geography-specific, i.e., they depend on empirical fitting and cannot be projected onto other geographical locations. In order to overcome this limitation, researchers resort to fit measurements that are obtained in different locations [16] at the cost of increased prediction error.…”

Section: A Related Workmentioning

confidence: 99%

“…The uncorrelated RV samples are passed through a correlation filter in order to introduce the appropriate correlation in the time-series. To create the filter that corresponds to the exponential autocorrelation model presented in (16), we follow the same steps derived in [43]. The chosen filter model depends on the nature of correlation that the channel has (e.g., Jakes filter is used to model the correlation for multi-path fading for land mobile satellite channels in ITU-R P.681 [44]).…”

Section: Satellite Channel Time-series Generationmentioning

confidence: 99%

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Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

Homssi¹,

Chan²,

Wang³

et al. 2022

Preprint

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<p>This paper presents a practical approach for Q/V-band modeling for low Earth orbit satellite channels based on tools from machine learning and statistical modeling. The developed Q/V-band LEO satellite channel model is presented in two folds; (i) a real-time forecasting method using model-based deep learning, intended for real-time operation of satellite terminals, and (ii) a statistical channel simulator that generates the path loss as a time-series random process, intended for system design and research. The provided approach capitalizes on real satellite measurements that are obtained from AlphaSat's Q/V-band transmitter at different geographic latitudes, to model the radio channel. The results show that model-based deep learning forecasting can outperform conventional statistically derived prediction methods for varying rain and elevation angle profiles. Moreover, it can also provide more accuracy in long-term prediction in comparison to current state-of-the-art machine learning approaches for radio channel prediction. Results for the statistical channel simulator is shown to produce synthetic radio excess path loss values for varying satellite passes by capitalizing on empirical statistical models obtained from real measurements.</p>

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Section: A Related Workmentioning

confidence: 99%

Section: Satellite Channel Time-series Generationmentioning

confidence: 99%

Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

Homssi¹,

Chan²,

Wang³

et al. 2022

Preprint

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“…This experiment complements another on-going one (running since 2015) at Ka - and Q -band measuring the beacons transmitted by AlphaSAT, as part of an Italian Space Agency/European Space Agency (ASI/ESA) experiment [ 1 , 2 , 3 ]. Due to the azimuth separation between KaSAT and AlphaSAT in Vigo, approximately

, we are also gathering orbit diversity information.…”

Section: Introductionmentioning

confidence: 99%

“…Further, actual identification of rain events and extraction of monthly, seasonal, or yearly statistics of first order (CDFs, cumulative distributions) or second order (time dependent) such as event durations, slopes, etc., need to be carried out, but are outside of the scope of this paper. For a detailed description of these tasks, refer, for example, to [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Ka-Band Satellite Receiver Data Preprocessing for Tropospheric Propagation Studies

Santos

Machado

Nandi

et al. 2022

Sensors

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Satellite tropospheric propagation studies strongly rely on beacon receiver measurements. We were interested in performing a measurement campaign to characterize rain attenuation statistics. In this article, we outline some of the characteristics and drawbacks one faces when trying to perform a radio wave satellite beacon propagation experiment at the Ka-band with low-cost measurement equipment. We used an affordable beacon receiver consisting of a commercial low-noise block down-converter, an outdoor dual-reflector antenna, and a software-defined radio unit. To measure rain attenuation events, we needed to work out where the reference signal level was at all times. However, as we did not have a radiometer to remove the impact of gases and clouds, since it is a very expensive device, we used a procedure that involved the subtraction of a stable and reliable reference level (template) from the raw received beacon level. This template was extracted from observations during non-rainy periods. The procedure implemented for extracting the template was based on the same data processing methodology used by other authors in this field. Here, we describe through specific examples the main characteristics of the templates extracted on non-rainy days, as well as the impact of some meteorological parameters and unavoidable, but small antenna pointing errors.

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“…A long tradition of propagation campaigns have been carried out since the 1970s: for example, Olympus F1 (beacons at Ku and Ka bands), Italsat 1 (Ka and Q bands) and the ongoing Alphasat Scientific Experiment (Ka and Q bands) 2,3 . All of these experiments contributed to the improvement of the Earth‐space channel characterization.…”

Section: Introductionmentioning

confidence: 99%

Preparation of a CubeSat LEO radio wave propagation campaign at Q and W bands

Cuervo

Martin-Polegre

Lasheras

et al. 2020

Satell Commun Network

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Due to the congestion of the spectrum and the need for wider bandwidths, ultra-high throughput satellites (UHTS) are moving towards the use of higher frequency bands (Q/V and W band). These frequencies are however severely impaired by atmospheric phenomena causing attenuation, scintillation and depolarization and compromising the quality of service (QoS). In order to study the Earth-space link, several experimental campaigns have been performed up to the Q band with geostationary (GEO) satellites. This paper introduces a new propagation experiment which will extend the characterization of the Earth-space channel up to the W band and low Earth orbits (LEO).

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Assessment of spatial and temporal properties of Ka/Q band earth‐space radio channel across Europe using Alphasat Aldo Paraboni payload

Cited by 19 publications

References 24 publications

Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

Deep Learning Forecasting and Statistical Modeling for Q/V-Band LEO Satellite Channels

Low-Cost Ka-Band Satellite Receiver Data Preprocessing for Tropospheric Propagation Studies

Preparation of a CubeSat LEO radio wave propagation campaign at Q and W bands

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