2 research outputs found
From MFN to SFN: Performance Prediction Through Machine Learning
In the last decade, the transition of digital terrestrial television (DTT) systems from multi-frequency networks (MFNs) to single-frequency networks (SFNs) has become a reality. SFN offers multiple advantages concerning MFN, such as more efficient management of the radioelectric spectrum, homogenizing the network parameters, and a potential SFN gain. However, the transition process can be cumbersome for operators due to the multiple measurement campaigns and required finetuning of the final SFN system to ensure the desired quality of service. To avoid time-consuming field measurements and reduce the costs associated with the SFN implementation, this paper aims to predict the performance of an SFN system from the legacy MFN and position data through machine learning (ML) algorithms. It is proposed a ML concatenated structure based on classification and regression to predict SFN electric-field strength, modulation error ratio, and gain. The model's training and test process are performed with a dataset from an SFN/MFN trial in Ghent, Belgium. Multiple algorithms have been tuned and compared to extract the data patterns and select the most accurate algorithms. The best performance to predict the SFN electric-field strength is obtained with a coefficient of determination (R2) of 0.93, modulation error ratio of 0.98, and SFN gain of 0.89 starting from MFN parameters and position data. The proposed method allows classifying the data points according to positive or negative SFN gain with an accuracy of 0.97
5G Radio Access Network Architecture for Terrestrial Broadcast Services
The 3rd Generation Partnership Project (3GPP) has defined based on the Long
Term Evolution (LTE) enhanced Multicast Broadcast Multimedia Service (eMBMS) a
set of new features to support the distribution of Terrestrial Broadcast
services in Release 14. On the other hand, a new 5th Generation (5G) system
architecture and radio access technology, 5G New Radio (NR), are being
standardised from Release 15 onwards, which so far have only focused on unicast
connectivity. This may change in Release 17 given a new Work Item set to
specify basic Radio Access Network (RAN) functionalities for the provision of
multicast/broadcast communications for NR. This work initially excludes some of
the functionalities originally supported for Terrestrial Broadcast services
under LTE e.g. free to air, receive-only mode, large-area single frequency
networks, etc. This paper proposes an enhanced Next Generation RAN architecture
based on 3GPP Release 15 with a series of architectural and functional
enhancements, to support an efficient, flexible and dynamic selection between
unicast and multicast/broadcast transmission modes and also the delivery of
Terrestrial Broadcast services. The paper elaborates on the Cloud-RAN based
architecture and proposes new concepts such as the RAN Broadcast/Multicast
Areas that allows a more flexible deployment in comparison to eMBMS. High-level
assessment methodologies including complexity analysis and inspection are used
to evaluate the feasibility of the proposed architecture design and compare it
with the 3GPP architectural requirements.Comment: 12 pages, 10 figures, 2 tables, IEEE Trans. Broadcastin