Statistical Characterization of Adjacent Satellite Interference for Earth Stations on Mobile Platforms Operating at Ku and Ka Bands

Kourogiorgas, Charilaos I.; Arapoglou, Pantelis-Daniel; Panagopoulos, Athanasios D.

doi:10.1109/lwc.2014.2374177

Cited by 16 publications

(7 citation statements)

References 8 publications

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“…From the time-series slow varying signal component 10 log 10 𝐿 𝑅 , we have obtained several features that includes-(1) time averaged mean of the signal (𝑋 𝑚 ) (2) standard deviation (𝑋 𝜎 ), (3) skewness (𝑋 𝑠 ), ( 4) kurtosis (𝑋 𝑘 ), ( 5) CCDF with the signal value exceeding 1, 2, 3, 4 dB and its mean value denoted in the respective order as 𝑋 𝑐 = [𝑋 1 𝑐 , 𝑋 2 𝑐 , 𝑋 3 𝑐 , 𝑋 4 𝑐 , 𝑋 𝑚 𝑐 ]. These extracted features from the time-series slow varying signal 10 log 10 𝐿 𝑅 is then fed into the SVM algorithm that predicts the fast varying signal parameters-bandwidth, mean 6 which shows that there is a good correlation between the variables in consideration.…”

Section: Svm Model and Predictionmentioning

confidence: 99%

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Modeling Short-Term Effects of Rain on Satellite Link using Machine Learning

Kumar¹

2022

Preprint

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<p>The received signal at the ground station for a satellite link is affected by the stochastic nature of atmospheric channel. Adverse weather events such as rain not only attenuates the signal but also increases noise, scintillation fading and multipath effects that cause rapid variations in the received signal at the ground station. In order to analyze the stochastic effects of weather phenomenon on the link performance on short-term basis, both the slowly changing signal attenuation and the rapid variations caused by channel at the receiver has to be studied. In this work, we first analyze the short-term effects of rain on the statistical and spectral properties of fast varying signal component affecting the link performance. Following this, we model such parameters using several features extracted from the slowly varying signal component with support vector machine (SVM). We show an interesting result that the statistical and spectral properties of fast varying signal under rainy channel conditions can be predicted with very high accuracy using SVM. The prediction of such parameters will lead to receiver design adaptive to varying channel dynamics affecting the link performance under rainy conditions.</p>

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Section: Svm Model and Predictionmentioning

confidence: 99%

“…T HE congestion at lower frequency bands requires that the satellite links are designed at higher frequency bands to meet the global requirements of growing consumer services at all places with all weather availability [1], [2]. However, the higher frequency bands above 10 GHz suffer more from the atmospheric effects [3].…”

Section: Introductionmentioning

confidence: 99%

Modeling Short-Term Effects of Rain on Satellite Link using Machine Learning

Kumar¹

2022

Preprint

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“…[ 3 Wireless Communications and Mobile Computing (a) Indicating equation ( 1), it can be seen that the CNIR depends on the CNR and CIR ratios. In addition, CIR in turn depends also on the INR ratio [8]: C/I (dB) = C/N (dB) −I/N (dB) this means that the CNIR criteria implicitly include all performance metrics (b) The CNIR has been used in many studies, such as [25] which concluded that at the frequency bands (Ku, K, and Ku), the interference problem of adjacent satellites, essentially clear for spotbeam/cell edge terminals, becomes more exacerbated by propagation effects, mainly attenuation by rain. In addition, the CNIR is of supreme significance in the design of new satellite broadband systems based on digital video broadcasting (DVB) (c) In cellular systems for example [26], the probability distribution of the CNIR has a significant task in performance evaluation of the system, managing of the radio resource, and planning of the radio network.…”

Section: System Protection Criteriamentioning

confidence: 99%

Performance of the DBS Satellite Receiver under the Impact of Rainfall and Terrestrial Interference

Shamsan

Al-Samman

2021

Wireless Communications and Mobile Computing

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This article presents a new study on the feasibility of operating a direct broadcasting satellite (DBS) system under the effect of both precipitation and interference from a fixed service (FS) at K-band in a semiarid region. The carrier-to-noise plus interference ratio (CNIR) as a protection criterion has been adopted to make sure that the receiver of the DBS system operates with an acceptable performance under rainfall and interference from FS. Various measured data for rainfall in different areas have been utilized to investigate different rain rate exceedance percentages. Results have been shown that areas with high rain rates have a small CNIR at the DBS receiver and require large protection distances compared to low-rain rate areas and vice versa. Some mitigation techniques have been suggested to alleviate the effect of rain and terrestrial interference on the DBS receiver performance.

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“…The framework of space-based AIS is shown in Figure 1. In Figure 1, due to the openness of satellite channels, satellite communication is easily disturbed by external signals, therefore, it is great significance to study the security of satellite communication in space-based AIS (Kourogiorgas et al, 2015) (Chen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

Zhu

Luo

et al. 2021

Front. Space Technol.

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In space-based AIS (Automatic Identification System), due to the high orbit and wide coverage of the satellite, there are many self-organizing communities within the observation range of the satellite, and the signals will inevitably conflict, which reduces the probability of ship detection. In this paper, to improve system processing power and security, according to the characteristics of neural network that can efficiently find the optimal solution of a problem, proposes a method that combines the problem of blind source separation with BP neural network, using the generated suitable data set to train the neural network, thereby automatically generating a traditional blind signal separation algorithm with a more stable separation effect. At last, through the simulation results of combining the blind source separation problem with BP neural network, the performance and stability of the space-based AIS can be effectively improved.

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Statistical Characterization of Adjacent Satellite Interference for Earth Stations on Mobile Platforms Operating at Ku and Ka Bands

Cited by 16 publications

References 8 publications

Modeling Short-Term Effects of Rain on Satellite Link using Machine Learning

Modeling Short-Term Effects of Rain on Satellite Link using Machine Learning

Performance of the DBS Satellite Receiver under the Impact of Rainfall and Terrestrial Interference

Design and Implementation of Blind Source Separation Based on BP Neural Network in Space-Based AIS

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