Enabling SFN Transmissions in 5G Cloud-RAN Deployments

Barjau, Carlos; Säily, Mikko; Barquero, David Gómez

doi:10.1109/bmsb47279.2019.8971859

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…However, the transition from a multi-frequency network (MFN) to an SFN might lead to multiple measurement campaigns and resource-consuming tuning processes to achieve the expected performance and quality of service (QoS). The above explanation justifies why, in the last years, several investigations have been oriented to exploit better and quantify the SFN capabilities beyond digital terrestrial television (DTT) and digital audio broadcasting [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 87%

“…It was introduced in Release 9 of the 3rd Generation Partnership Project (3GPP) as a multimedia broadcast single frequency network (MBSFN), where the same content is transmitted to a group of users in a cell using a subset of available resources [23]. Likewise, SFN is determinant for the broadcast/multicast services over 5G networks, reducing interference and handover rates between physical cells and contributing to the users' QoS [8].…”

Section: A Sfn Planningmentioning

confidence: 99%

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From MFN to SFN: Performance Prediction Through Machine Learning

Gonzalez

Pupo

Pereira-Ruisánchez³

et al. 2022

IEEE Trans. on Broadcast.

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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 (R 2 ) 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 SFNgain with an accuracy of 0.97.

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Section: Introductionmentioning

confidence: 87%

Section: A Sfn Planningmentioning

confidence: 99%

From MFN to SFN: Performance Prediction Through Machine Learning

Gonzalez

Pupo

Pereira-Ruisánchez³

et al. 2022

IEEE Trans. on Broadcast.

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“…However, the transition from a multi frequency network (MFN) to an SFN involves multiple measurement campaigns and tuning of the network to achieve the expected outperformance and quality of service (QoS). This is why, in the last years, several investigations such as [4][5][6][7][8][9][10][11][12] have been oriented to better exploit and quantify the capabilities of SFN.…”

Section: Introductionmentioning

confidence: 99%

Three-stages concatenated Machine Learning model for SFN prediction

Gonzalez

Pupo

Pereira-Ruisánchez³

et al. 2021

2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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The single frequency network (SFN) has been assumed worldwide by telecommunication operators to save radio frequency resources and homogenize the network. Its applications have transcended the digital terrestrial television and digital radio to become part of the key techniques of the broadband and broadcast convergence for LTE-A, 5G and beyond. However, the transition from a multi frequency network (MFN) to an SFN involves multiple measurement campaigns and tuning of the network to achieve the expected up-performance and quality of service. This paper aims to propose a machine learning model to predict the SFN performance from the legacy MFN parameters. The model is based on regression and classification machine learning algorithms concatenated in three consecutive stages to predict SFN electric-field strength, modulation error ratio and gain. The training and test processes are performed with a dataset of 389 samples from an SFN/MFN trial in Ghent, Belgium. The best performance is obtained with concatenating gradient boosting, random forest, and linear regression, which allows predicting the SFN electric-field strength with an R 2 of 92%, the modulation error ratio with 95%, and SFN gain with 87% from only MFN and position data. Besides, the model allows classifying the data points according to positive or negative SFN gain with an accuracy of 93%.

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“…Broadcast/multicast SFN transmission enabled gNB uses the SDAP layer where the modified gNB-CU-MC will receive the broadcast/multicast traffic and identify the type of traffic either based on the QoS flow identifier or through the context associ- The latency difference between different DU paths is compensated by using RAN-SYNC. Extracted from [68] ated with the flow. In other words, there is a mapping between a QoS flow identifier and a broadcast/multicast service.…”

Section: G-xcast Ran Architecturementioning

confidence: 99%

“…Two SFNs are shown, with different number of DUs involved in the synchronized transmission.The latency difference between different DU paths is compensated by using RAN-SYNC. Extracted from[68]…”

mentioning

confidence: 99%

Point-to-Multipoint Services on Fifth-Generation Mobile Networks

Estevan¹

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Point-to-multipoint services over mobile networks have always been of interest to several verticals. They are not only limited to the delivery of television, but other applications such as Internet of Things (IoT), Vehicular-to-Everything, Public Warning Services (PWS); which can efficiently leverage the capacity that the use of point-of-multipoint provides. In this regard, mobile networks had their first version of a Broadcast/Multicast extension in 3G, known as Multicast/Broadcast Multimedia Services (MBMS). However, this technology was considered limited in technical and economical terms and did never successfully take off. The 4G Long Term Evolution (LTE) version, enhanced MBMS (eMBMS), provided the much needed enhancements by listening to the requirements of the broadcast industry. Broadcasters, specially in Europe, see in this technology a potential terrestrial broadcast standard able to provision point-tomultipoint services for both rooftop antennas, pedestrian devices and moving vehicles. Yet, the requirement of New Radio based point-to-multipoint mode was defined in the early stages of 5G, but it was not until Release 17 that this requirement started to get addressed, in the form of 5G Multicast Broadcast Services (5MBS). These point-to-multipoint mobile standards compete against the existing European standard DVB-T2 and the American ATSC 3.0, already deployed commercially. It remains to be seen what will happen to the lower band of the UHF, which is currently allocated to primary broadcast services in ITU region 1 and an agenda topic for the World Radio Congress of 2023. In other regions, part of the band has been allocated to 5G communications.This dissertation covers the state-of-the-art in LTE eMBMS Release 14, also known as Enhanced Television Services (ENTV). ENTV provided a suite of radio and core enhancements that made eMBMS into a viable terrestrial broadcast standard. The latest iteration of this technology is known as LTEbased 5G Broadcast; even though it is not New Radio or 5G Core based. To bridge this gap, research efforts by academia, public and private enterprises evaluated how to provide a 5G-based solution for point-to-multipoint services. The most notable effort in this regard is the Horizon 2020 project 5G-Xcast,

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Enabling SFN Transmissions in 5G Cloud-RAN Deployments

Cited by 6 publications

References 7 publications

From MFN to SFN: Performance Prediction Through Machine Learning

From MFN to SFN: Performance Prediction Through Machine Learning

Three-stages concatenated Machine Learning model for SFN prediction

Point-to-Multipoint Services on Fifth-Generation Mobile Networks

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