Modulation Error Ratio Gain of Single Frequency Network in DVB-T2

Promwong, Sathaporn; Tiengthong, Thanadol; Ruckveratham, Bundit

doi:10.1109/ecti-ncon.2019.8692273

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Additional works regarding the evolutionary state-of-art are proposed in [3][4][5][6][7]. In [3], the author presents a design performed for the DVB-T digital terrestrial television network in Greece.…”

Section: Channel Characteristics For Fixed and Portable Dtv Receptionmentioning

confidence: 99%

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Channel Characteristics for Fixed and Portable DTV Reception in a Single Frequency Network

Guerra

Filho

Ron

et al. 2019

J. Microw. Optoelectron. Electromagn. Appl.

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This paper presents the propagation channel characteristics of a digital TV single-frequency network (SFN) obtained by carrying out field measurements using two synchronized transmitters. The measurements are performed at 31 reception points using both a directive reception antenna, which is typical of fixed reception scenarios, and an omnidirectional antenna, which is used to receive mobile signals. The characteristic parameters of the channel are obtained, including the average delay, the root mean square (RMS) delay spread, and the Rician K-factor, which are important for the design of SFN systems. An empirical expression is obtained for the prediction of the RMS delay spread as a function of the K factor and the distances to the transmission antennas.

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Section: Channel Characteristics For Fixed and Portable Dtv Receptionmentioning

confidence: 99%

“…In [6], the SFN threshold reception for broadcasting is obtained by analyzing and evaluating the effects of the delay time between two SFN transmitters within the guard interval time. The minimum reception threshold in single-input-singleoutput (SISO) mode SFN broadcasting is analyzed in [7].…”

Section: Channel Characteristics For Fixed and Portable Dtv Receptionmentioning

confidence: 99%

Channel Characteristics for Fixed and Portable DTV Reception in a Single Frequency Network

Guerra

Filho

Ron

et al. 2019

J. Microw. Optoelectron. Electromagn. Appl.

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“…However, the transition from a multi frequency network (MFN) to an SFN involves multiple measurement campaigns and tuning of the network to achieve the expected outperformance and quality of service (QoS). This is why, in the last years, several investigations such as [4][5][6][7][8][9][10][11][12] have been oriented to better exploit and quantify the capabilities of SFN.…”

Section: Introductionmentioning

confidence: 99%

Three-stages concatenated Machine Learning model for SFN prediction

Gonzalez

Pupo

Pereira-Ruisánchez³

et al. 2021

2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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The single frequency network (SFN) has been assumed worldwide by telecommunication operators to save radio frequency resources and homogenize the network. Its applications have transcended the digital terrestrial television and digital radio to become part of the key techniques of the broadband and broadcast convergence for LTE-A, 5G and beyond. However, the transition from a multi frequency network (MFN) to an SFN involves multiple measurement campaigns and tuning of the network to achieve the expected up-performance and quality of service. This paper aims to propose a machine learning model to predict the SFN performance from the legacy MFN parameters. The model is based on regression and classification machine learning algorithms concatenated in three consecutive stages to predict SFN electric-field strength, modulation error ratio and gain. The training and test processes are performed with a dataset of 389 samples from an SFN/MFN trial in Ghent, Belgium. The best performance is obtained with concatenating gradient boosting, random forest, and linear regression, which allows predicting the SFN electric-field strength with an R 2 of 92%, the modulation error ratio with 95%, and SFN gain with 87% from only MFN and position data. Besides, the model allows classifying the data points according to positive or negative SFN gain with an accuracy of 93%.

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Modulation Error Ratio Gain of Single Frequency Network in DVB-T2

Cited by 2 publications

References 7 publications

Channel Characteristics for Fixed and Portable DTV Reception in a Single Frequency Network

Channel Characteristics for Fixed and Portable DTV Reception in a Single Frequency Network

Three-stages concatenated Machine Learning model for SFN prediction

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