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Your search keyword '"Nieto, Glauco Guillen"' showing total 13 results
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13 results on '"Nieto, Glauco Guillen"'

1. Multi-objective Optimization of Cognitive Radio Networks

Author

Alonso, Rodney Martinez, Plets, David, Deruyck, Margot, Martens, Luc, Nieto, Glauco Guillen, and Joseph, Wout

Subjects
Computer Science - Networking and Internet Architecture
Abstract

New generation networks, based on Cognitive Radio technology, allow dynamic allocation of the spectrum, alleviating spectrum scarcity. These networks also have a resilient potential for dynamic operation for energy saving. In this paper, we present a novel wireless network optimization algorithm for cognitive radio networks based on a cloud sharing-decision mechanism. Three Key Performance Indicators (KPIs) were optimized: spectrum usage, power consumption, and exposure of human beings. For a realistic suburban scenario in Ghent city, Belgium, we determine the optimality among the KPIs. Compared to a traditional Cognitive Radio network design, our optimization algorithm for the cloud-based architecture reduced the network power consumption by 27.5%, the average global exposure by 34.3%, and spectrum usage by 34.5% at the same time. Even for the worst optimization case, our solution performs better than the traditional architecture by 4.8% in terms of network power consumption, 7.3% in terms of spectrum usage and 4.3% in terms of global exposure.

Published
2024
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3. 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

7. Experimental Assessment of Harmful Interference Between Broadcasting and Distributed Cognitive Radio Networks in Real Scenarios

Author

Alonso, Rodney Martinez, primary, Plets, David, additional, Guerra, Arley Coto, additional, Santana, Yosvany Hervis, additional, Pupo, Ernesto Fontes, additional, Garcia, Alejandro Gonzalez, additional, Alvarez, Rufino Cabrera, additional, Nieto, Glauco Guillen, additional, Martens, Luc, additional, and Joseph, Wout, additional

Published
2020
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9. 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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