A Dynamic MBSFN Area Formation Algorithm for Multicast Service Delivery in 5G NR Networks

Araniti, Giuseppe; Rinaldi, Federica; Scopelliti, Pasquale; Molinaro, Antonella; Iera, Antonio

doi:10.1109/twc.2019.2948846

Cited by 24 publications

(8 citation statements)

References 13 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%

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%

From MFN to SFN: Performance Prediction Through Machine Learning

Gonzalez

Pupo

Pereira-Ruisánchez³

et al. 2022

IEEE Trans. on Broadcast.

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“…Then, a particular scenario where subgrouping techniques have had a very positive reception is eMBMS. Particularly, authors in [15] presented a heuristic algorithm for eMBMS environments over single frequency networks (MBSFN). In this case, the work studied the area formation in MBSFN to increase the ADR exploiting the multi-rate transmissions.…”

Section: B Related Workmentioning

confidence: 99%

Nonorthogonal Multiple Access and Subgrouping for Improved Resource Allocation in Multicast 5G NR

Iradier

Fadda

Murroni

et al. 2022

IEEE Open J. Commun. Soc.

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The ever-increasing demand for applications with stringent constraints in device density, latency, user mobility, or peak data rate has led to the appearance of the last generation of mobile networks (i.e., 5G). However, there is still room for improvement in the network spectral efficiency, not only at the waveform level but also at the Radio Resource Management (RRM). Up to now, solutions based on multicast transmissions have presented considerable efficiency increments by successfully implementing subgrouping strategies. These techniques enable more efficient exploitation of channel time and frequency resources by splitting users into subgroups and applying independent and adaptive modulation and coding schemes. However, at the RRM, traditional multiplexing techniques pose a hard limit in exploiting the available resources, especially when users' QoS requests are unbalanced. Under these circumstances, this paper proposes jointly applying the subgrouping and Non-Orthogonal Multiple Access (NOMA) techniques in 5G to increase the network data rate. This study shows that NOMA is highly spectrum-efficient and could improve the system throughput performance in certain conditions. In the first part of this paper, an in-depth analysis of the implications of introducing NOMA techniques in 5G subgrouping at RRM is carried out. Afterward, the validation is accomplished by applying the proposed approach to different 5G use cases based on vehicular communications. After a comprehensive analysis of the results, a theoretical approach combining NOMA and time division is presented, which improves considerably the data rate offered in each use case.

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“…Consequently, it considerably improves the bandwidth efficiency compared to unicast mode since all users are served simultaneously by using a single wide transmission beam (omnidirectional), which generally provides very short transmission duration. In the past literature, several efficient solutions improving traditional multicast schemes in omnidirectional networks were proposed [12][13][14]. However, pure multicast schemes are almost infeasible in mmWave directional systems due to the propagation specifics at EHF bands.…”

Section: Multicast In Traditional Networkmentioning

confidence: 99%

Challenges and Performance Evaluation of Multicast Transmission in 60 GHz mmWave

Chukhno

Araniti

et al. 2020

Communications in Computer and Information Science

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This publication is copyrighted. You may download, display and print it for Your own personal use. Commercial use is prohibited. Take down policyIf you believe that this document breaches copyright, please contact the authors, and we will investigate your claim. BibTex entry @inproceedings{chukhno2020challenges,title={Challenges and Performance Evaluation of Multicast Transmission in 60 GHz mmWave}, author={Chukhno

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A Dynamic MBSFN Area Formation Algorithm for Multicast Service Delivery in 5G NR Networks

Cited by 24 publications

References 13 publications

From MFN to SFN: Performance Prediction Through Machine Learning

From MFN to SFN: Performance Prediction Through Machine Learning

Nonorthogonal Multiple Access and Subgrouping for Improved Resource Allocation in Multicast 5G NR

Challenges and Performance Evaluation of Multicast Transmission in 60 GHz mmWave

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