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Your search keyword '"Hervis Santana, Yosvany"' showing total 5 results
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5 results on '"Hervis Santana, Yosvany"'

1. Radio Environment Map of an LTE Deployment Based on Machine Learning Estimation of Signal Levels

Author

Hervis Santana, Yosvany, Plets, David, Martinez Alonso, Rodney, Nieto, Glauco Guillen, Martens, Luc, and Joseph, Wout

Subjects
Machine Learning, Technology and Engineering, Coverage, REM, Received Signal, Estimation
Abstract

Accurate estimation of Propagation Path Loss is important for reliable and optimized coverage of a service. In literature, a diversity of theoretically or experimentally based propagation models have been documented to estimate the received signal level. The goal of this work is to estimate the effective coverage area of service, predict the Path Loss, and build a Radio Environment Map (REM) using a sensor network. To this end, a sensor's correlation area is defined. By using Machine Learning (ML), the received signal level variation in this area can be estimated correctly 92.3% of the time, with a Mean Absolute Error (MAE) of 1.57 dB. Finally, a proper distribution of sensors based on the correlation area, and ML tools leads to building a REM for the effective coverage area. This approach is applied to a Long-Term Evolution network.

Published
2022

3. SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems.

Author

Hervis Santana, Yosvany, Plets, David, de Pessemier, Toon, Alonso, Rodney Martinez, Nieto, Glauco Guillen, Martens, Luc, and Joseph, Wout

Subjects
*SINGLE frequency network, *TELEVISION broadcasting, *MACHINE learning, *ABSOLUTE value, *MULTICASTING (Computer networks)
Abstract

LTE-eMBMS systems efficiently deliver multicast/broadcast services using Layered Division Multiplexing (LDM) technology. In a two-layer LDM system, Layer 1, with higher power allocation delivers mobile services, and Layer 2 in a Single Frequency Network scheme provides local content. The challenge is to reduce the gap in the layers’ coverage areas caused by the use of different constellations, and SFN gain for Layer 2. Hence, the precision in the coverage area estimation is crucial for the successful planning and deployment, particularly regarding the SFN gain contribution in Layer 2. For this purpose, a real digital TV broadcasting SFN system was used as a model to design a method based on Machine Learning algorithms, aiming to enhance the coverage area precision for the Layer 2 in eMBMS. The method is able to estimate SFN gain value with a Mean Absolute Error (MAE) of 0.72 dB and certainty in positive or negative contribution in 93% of the cases. [ABSTRACT FROM AUTHOR]

Published
2022
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5. Calibración y ajuste de mediciones del nivel de potencia en herramienta de monitoreo DTMB basada en STB comercial.

Author

Morejón Betancourt, Dreyelian and Hervis Santana, Yosvany

Subjects
*DIGITAL television, *CALIBRATION, *TELECOMMUNICATION, *TELEVISION receivers, *SIGNALS & signaling, *TELEVISION broadcasting
Abstract

Currently in LACETEL®, Research & Development Telecommunications Institute, it is being developed a tool for monitoring DTMB digital TV signal, using the hardware of a commercial receiver. Since the final result is to transform a TV receiver into a measuring instrument, this implies the need for its calibration and adjustment for each of the physical parameters to be measured. The present work focuses on the calibration and adjustment of the instrument for the measurement of the received signal's power. After performing an initial calibration, the results of the measurements obtained for each channel are analyzed in function of the reference signal. A functional relationship between the values is identified, the measurements are adjusted using mathematical regression techniques and the obtained adjustments are built into the instrument. The outcome of the adjustment is validated through a new calibration, verifying a reduction in the systematic errors of the instrument. [ABSTRACT FROM AUTHOR]

Published
2018

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