ATSC 3.0 Broadcast 5G Unicast Heterogeneous Network Converged Services Starting Release 16

Simon, Michael; Kofi, Ebenezer; Libin, Louis; Aitken, Mark

doi:10.1109/tbc.2020.2985575

Cited by 43 publications

(18 citation statements)

References 9 publications

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“…The Mean Absolute Error (MAE) is used to control the training process and have a result in the validation process. This value is calculated by employing (3).…”

Section: F Model Evaluationmentioning

confidence: 99%

“…eMBMS are delivered in sub-frames of the available Physical Resource Blocks in TDM mode [2]. However, the inclusion of LDM in 5G-MBMS [3], enables the provision of unicast, multicast or broadcasting services, multiplexed across the multi-layer architecture. In [4], the authors proposed the application of Layered Division Multiplexing (LDM) technology to the LTE-eMBMS.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems

Santana¹,

David

Pessemier

et al. 2022

IEEE Trans. on Broadcast.

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“…The Mean Absolute Error (MAE) is used to control the training process and have a result in the validation process. This value is calculated by employing (3).…”

Section: F Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems

Santana¹,

David

Pessemier

et al. 2022

IEEE Trans. on Broadcast.

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“…Additionally, for a limited backhaul capacity network, some efficient spectrum allocation solution is required to provide significant performance improvement in throughput enhancement, delay reduction, and energy savings for small-cell networks [259]. The switching between small cells and Wi-Fi remains a challenging task that is being explored in the latest 3GPP releases [260]. Furthermore, in small cell-based HetNet, which consists of multilayers with a shared spectrum, a dynamic spectrum and multicell logarithmic resource allocation algorithm are required [261].…”

Section: Small Cellmentioning

confidence: 99%

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

et al. 2020

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With an extensive growth in user demand for high throughput, large capacity, and low latency, the ongoing deployment of Fifth-Generation (5G) systems is continuously exposing the inherent limitations of the system, as compared with its original premises. Such limitations are encouraging researchers worldwide to focus on next-generation 6G wireless systems, which are expected to address the constraints. To meet the above demands, future radio network architecture should be effectively designed to utilize its maximum radio spectrum capacity. It must simultaneously utilize various new techniques and technologies, such as Carrier Aggregation (CA), Cognitive Radio (CR), and small cell-based Heterogeneous Networks (HetNet), high-spectrum access (mmWave), and Massive Multiple-Input-Multiple-Output (M-MIMO), to achieve the desired results. However, the concurrent operations of these techniques in current 5G cellular networks create several spectrum management issues; thus, a comprehensive overview of these emerging technologies is presented in detail in this study. Then, the problems involved in the concurrent operations of various technologies for the spectrum management of the current 5G network are highlighted. The study aims to provide a detailed review of cooperative communication among all the techniques and potential problems associated with the spectrum management that has been addressed with the possible solutions proposed by the latest researches. Future research challenges are also discussed to highlight the necessary steps that can help achieve the desired objectives for designing 6G wireless networks.

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“…In the current LTE network, the Evolved Multimedia Broadcast Multicast Services (LTE eMBMS) are delivered in eMBMS sub-frames in the available Physical Resource Blocks (PRB) in TDM mode [7]. However, the inclusion of LDM in 5G-MBMS [8], [9] enables the provision of unicast, multicast or broadcasting services, multiplexed across the multi-layer architecture.…”

Section: Introductionmentioning

confidence: 99%

Experimental Assessment of New Generation Radio Networks Based on Layered Division Multiplexing

Alonso¹,

David²,

Deruyck³

et al. 2022

IEEE Trans. on Broadcast.

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Despite the recent advances in broadband penetration and accessibility, broadcasting networks continue to be the most efficient way for delivering media content to large areas independently of the user density. Nevertheless, a convergence of broadcasting services into the broadband networks is foreseen. In this context, Layered Division Multiplexing (LDM) allows the joint provision of unicast, multicast, and broadcast services in mobile cells infrastructure. Despite the success of LDM in broadcasting infrastructure, its practical application in Long Term Evolution networks providing Evolved Multimedia Broadcast Multicast Services (LTE eMBMS) and 5G-MBMS is more difficult. In this paper, we experimentally quantify the cellinterference and its impact on the network performance and Quality of Service. Our experiments reveal that the inter-cell interference margin for LTE eMBMS is about 3 dB higher compared to traditional LTE networks. If a layered architecture is incorporated this higher inter-cell interference would cause a reduction of approximately 19% of the Enhanced Layer (Lower Layer) coverage.

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ATSC 3.0 Broadcast 5G Unicast Heterogeneous Network Converged Services Starting Release 16

Cited by 43 publications

References 9 publications

SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems

SFN Gain Prediction by Neural Networks for Enhancing Layer 2 Coverage in LDM Systems

Issues, Challenges, and Research Trends in Spectrum Management: A Comprehensive Overview and New Vision for Designing 6G Networks

Experimental Assessment of New Generation Radio Networks Based on Layered Division Multiplexing

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