SFN gain prediction by neural networks for enhancing Layer 2 coverage in LDM systems
- Author
- Yosvany Hervis Santana (UGent) , David Plets (UGent) , Toon De Pessemier (UGent) , Rodney Martinez Alonso (UGent) , Glauco Guillen Nieto, Luc Martens (UGent) and Wout Joseph (UGent)
- Organization
- Project
- 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.
- Keywords
- Transmitters, Gain, Long Term Evolution, Training, Gain measurement, Layered division multiplexing, Trajectory, eMBMS, LTE, LDM, layers', coverage gap, machine learning, SFN, PROPAGATION, ALGORITHM
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01GJFZ00GG6VES9BZVSFENSDXK
- MLA
- Hervis Santana, Yosvany, et al. “SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems.” IEEE TRANSACTIONS ON BROADCASTING, vol. 68, no. 1, 2022, pp. 171–79, doi:10.1109/TBC.2021.3113277.
- APA
- Hervis Santana, Y., Plets, D., De Pessemier, T., Martinez Alonso, R., Nieto, G. G., Martens, L., & Joseph, W. (2022). SFN gain prediction by neural networks for enhancing Layer 2 coverage in LDM systems. IEEE TRANSACTIONS ON BROADCASTING, 68(1), 171–179. https://doi.org/10.1109/TBC.2021.3113277
- Chicago author-date
- Hervis Santana, Yosvany, David Plets, Toon De Pessemier, Rodney Martinez Alonso, Glauco Guillen Nieto, Luc Martens, and Wout Joseph. 2022. “SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems.” IEEE TRANSACTIONS ON BROADCASTING 68 (1): 171–79. https://doi.org/10.1109/TBC.2021.3113277.
- Chicago author-date (all authors)
- Hervis Santana, Yosvany, David Plets, Toon De Pessemier, Rodney Martinez Alonso, Glauco Guillen Nieto, Luc Martens, and Wout Joseph. 2022. “SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems.” IEEE TRANSACTIONS ON BROADCASTING 68 (1): 171–179. doi:10.1109/TBC.2021.3113277.
- Vancouver
- 1.Hervis Santana Y, Plets D, De Pessemier T, Martinez Alonso R, Nieto GG, Martens L, et al. SFN gain prediction by neural networks for enhancing Layer 2 coverage in LDM systems. IEEE TRANSACTIONS ON BROADCASTING. 2022;68(1):171–9.
- IEEE
- [1]Y. Hervis Santana et al., “SFN gain prediction by neural networks for enhancing Layer 2 coverage in LDM systems,” IEEE TRANSACTIONS ON BROADCASTING, vol. 68, no. 1, pp. 171–179, 2022.
@article{01GJFZ00GG6VES9BZVSFENSDXK,
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.}},
author = {{Hervis Santana, Yosvany and Plets, David and De Pessemier, Toon and Martinez Alonso, Rodney and Nieto, Glauco Guillen and Martens, Luc and Joseph, Wout}},
issn = {{0018-9316}},
journal = {{IEEE TRANSACTIONS ON BROADCASTING}},
keywords = {{Transmitters,Gain,Long Term Evolution,Training,Gain measurement,Layered division multiplexing,Trajectory,eMBMS,LTE,LDM,layers',coverage gap,machine learning,SFN,PROPAGATION,ALGORITHM}},
language = {{eng}},
number = {{1}},
pages = {{171--179}},
title = {{SFN gain prediction by neural networks for enhancing Layer 2 coverage in LDM systems}},
url = {{http://doi.org/10.1109/TBC.2021.3113277}},
volume = {{68}},
year = {{2022}},
}
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