SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems
| Autor: | Yosvany Hervis Santana, Toon De Pessemier, Rodney Martinez Alonso, David Plets, Glauco Guillen Nieto, Luc Martens, Wout Joseph |
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| Rok vydání: | 2022 |
| Předmět: |
Technology and Engineering
Layered division multiplexing Artificial neural network Computer science Trajectory LDM coverage gap PROPAGATION layers' Transmitters LTE machine learning eMBMS SFN Long Term Evolution Media Technology Electronic engineering Training ALGORITHM Gain Electrical and Electronic Engineering Layer (object-oriented design) Gain measurement |
| Zdroj: | IEEE TRANSACTIONS ON BROADCASTING |
| ISSN: | 1557-9611 0018-9316 |
| DOI: | 10.1109/tbc.2021.3113277 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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