Overview of Fading Channel Modeling

Noga, Krystyna; Pałczyńska, Beata

doi:10.2478/v10177-010-0044-x

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…where g i,j is a Nakagami-m fading channel [17]. The Nakagami factor m means the ratio of line-of-sight and non-line-of-sight components.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

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Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

Kim

2020

Electronics

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The proximity-based device-to-device (D2D) communication allows for internet of things, public safety, and data offloading services. Because of these advantages, D2D communication has been applied to wireless communication networks. In wireless networks using D2D communication, there are challenging problems of the data rate shortage and coverage limitation due to co-channel interference in the proximity communication. To resolve the problems, transmit power control schemes that are based on deep learning have been presented in network-assisted D2D communication systems. The power control schemes have focused on enhancing spectral efficiency and energy efficiency in the presence of interference. However, the data-rate fairness performance may be a key performance metric in D2D communications, because devices in proximity can expect fair quality of service in the system. Hence, in this paper, a transmit power control scheme using a deep-learning algorithm based on convolutional neural network (CNN) is proposed to consider the data-rate fairness performance in network-assisted D2D communication systems, where the wireless channels are modelled by path loss and Nakagami fading. In the proposed scheme, the batch normalization (BN) scheme is introduced in order to further enhance the spectral efficiency of the conventional deep-learning transmit power control scheme. In addition, a loss function for the deep-learning optimization is defined in order to consider both the data-rate fairness and spectral efficiency. Through simulation, we show that the proposed scheme can achieve extremely high fairness performance while improving the spectral efficiency of the conventional schemes. It is also shown that the improvement in the fairness and spectral efficiency is achieved for different Nakagami fading conditions and sizes of area containing the devices.

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“…where g i,j is a Nakagami-m fading channel [17]. The Nakagami factor m means the ratio of line-of-sight and non-line-of-sight components.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

“…, where R n and I n are independent Gaussian processes with zero mean and unit variance [17], and the channel samples for all of the links are independently generated. The batch size is set to 500 and the learning rate is set to 0.00005.…”

Section: Performance Evaluationmentioning

confidence: 99%

Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

Kim

2020

Electronics

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“…The Weibull distribution is one of the most commonly used distributions with a wide range of applications in some study fields such as: chemical engineering (Chiang et al (2004), Kuo-Chao et al (2009), and Wood et al (2005)), ecology Stankova and Zlatanov (2010), electrical engineering (Genc et al (2005) and Pascual (2006)), food industry Corzo et al (2008), mechanical engineering (Raghunathan et al (2002) and Lavanya et al (2016)), telecommunications (Surendran et al (2014) and Buller et al (2013)), wireless communications Noga and Palczynska (2007), economic (Nadarajah and Kotz (2006) and Diaconu (2009)), civil engineering (Muraleedharan et al (2007) and Arenas et al (2010)), and seismology Hasumi et al (2009). For further details on applications of the Weibull distribution, we refer the readers to Meeker and Escobar (1998), Murthy et al (2004), and Dodson (2006).…”

Section: Introductionmentioning

confidence: 99%

New Estimators for Weibull Distribution Parameters: Comprehensive Comparative Study for Weibull Distribution

Sadani¹,

Abdollahnezhad²,

Teimouri³

et al. 2019

JSRI

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“…The Weibull distribution is one of the most commonly used distributions with a wide range of applications in some study fields such as: chemical engineering ( [3], [22], and [41]), ecology [33], electrical engineering ( [11] and [30]), food industry [4], mechanical engineering ( [31] and [23]), telecommunications ([34] and [2]), wireless communications [28], economic ( [27] and [7]), civil engineering ( [26] and [1]), and seismology [15]. For further details on applications of the Weibull distribution, we refer the readers to Meeker and Escobar (1998), Murthy et al (2004), and Dodson (2006).…”

Section: Introductionmentioning

confidence: 99%

A new estimator for Weibull distribution parameters: Comprehensive comparative study for Weibull Distribution

Sadani¹,

Abdollahnezhad²,

Teimouri³

et al. 2019

Preprint

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Weibull distribution has received a wide range of applications in engineering and science. The utility and usefulness of an estimator is highly subject to the field of practitioner's study. In practice users looking for their desired estimator under different setting of parameters and sample sizes. In this paper we focus on two topics. Firstly, we propose U -statistics for the Weibull distribution parameters. The consistency and asymptotically normality of the introduced U -statistics are proved theoretically and by simulations. Several of methods have been proposed for estimating the parameters of Weibull distribution in the literature. These methods include: the generalized least square type 1, the generalized least square type 2, the L-moments, the Logarithmic moments, the maximum likelihood estimation, the method of moments, the percentile method, the weighted least square, and weighted maximum likelihood estimation. Secondary, due to lack of a comprehensive comparison between the Weibull distribution parameters estimators, a comprehensive comparison study is made between our proposed U -statistics and above nine estimators. Based on simulations, it turns out that the our proposed U -statistics show the best performance in terms of bias for estimating the shape and scale parameters when the sample size is large.

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Overview of Fading Channel Modeling

Abstract: Abstract-VisSim and LabVIEW based approaches are proposed and implemented to demonstrate fading in the communication systems. The introduction to fading, models for flat fading like Rayleigh, Weibull, Nakagami-m and the results of simulations are presented.

Cited by 7 publications

References 8 publications

Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

Deep Learning-Based Power Control Scheme for Perfect Fairness in Device-to-Device Communication Systems

New Estimators for Weibull Distribution Parameters: Comprehensive Comparative Study for Weibull Distribution

A new estimator for Weibull distribution parameters: Comprehensive comparative study for Weibull Distribution

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