Networking Challenges for Non-Terrestrial Networks Exploitation in 5G

Bacco, Manlio; Davoli, Franco; Giambene, Giovanni; Gotta, Alberto; Luglio, M.; Marchese, Mario; Patrone, Fabio; Roseti, C.

doi:10.1109/5gwf.2019.8911669

Cited by 33 publications

(19 citation statements)

References 17 publications

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“…This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and non-terrestrial networks [7], [8] based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times in QUIC connections.…”

Section: Introductionmentioning

confidence: 99%

Exploring Machine Learning for Classification of QUIC Flows over Satellite

Secchi

Cassarà

Gotta

2022

ICC 2022 - IEEE International Conference on Communications

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Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.

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Section: Introductionmentioning

confidence: 99%

Exploring Machine Learning for Classification of QUIC Flows over Satellite

Secchi

Cassarà

Gotta

2022

ICC 2022 - IEEE International Conference on Communications

Self Cite

View full text Add to dashboard Cite

show abstract

“…by assuming independent features. They also provide the incremental version of the previous algorithm, as shown in equation (3).…”

Section: B Feature Selection Algorithmmentioning

confidence: 99%

“…We analyzed the performance of the proposed crossentropy-based (CE) algorithm for feature selection from the QUIC dataset. We compared the performance of CE in detecting the proportion of QUIC traffic versus non-QUIC traffic in a sample of traffic outages with the performance of other information-based feature selection algorithms such as mRMR [30], CMIM [31], and DSR [32], which provide the solution to the optimization problem in equation (3). We also compared the cross-entropy based algorithm with the analysis of variance technique.…”

Section: Numerical Analysismentioning

confidence: 99%

“…However, these 3D networks pose a challenge to the different levels of Quality of Service (QoS) requirements that each network technology can provide. For instance, while the satellite link can provide bandwidth required for an eMBB-type service, it is not guaranteed that it can satisfy the latency constraints expected in 5G for URLLC traffic [3]. Therefore, service classification and separation are of utmost importance to guarantee the required QoS level to a given traffic flow through the suitable network trunk.…”

Section: Introductionmentioning

confidence: 99%

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Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks

Bano¹,

Machumilane²,

Valerio³

et al. 2022

Preprint

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Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-toend packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required QoS level to each traffic sub-flow through the appropriate network trunk. In this paper, a federated feature selection and feature reduction learning scheme is proposed to classify network traffic in a semisupervised cooperative manner. The federated gateways of 3D network help to enhance the global knowledge of network traffic to improve the accuracy of anomaly and intrusion detection and service identification of a new traffic flow.

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“…To not compromise the C-RAN resources and in order to reduce communications to/from the cloud, edge computing has emerged to exploit the distributed computing resources in close proximity to users. With C-RAN and edge computing, aerial/satellite networks can adhere to the 3GPP 5G cellular access network standard [336] which seems to be a key factor for the final integration into 5G [337].…”

Section: Higher Layer Advancementsmentioning

confidence: 99%

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Solanki¹,

Chatzinotas²,

Kodheli³

et al. 2021

Preprint

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Non-terrestrial networks (NTNs) traditionally had certain limited applications. However, the recent technological advancements opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting its relevance to 5G networks and essentially, how it will play a pivotal role for the development of 6G and beyond wireless networks. The survey discusses important features of NTNs integration into TNs by delving into the new range of services and use cases, various architectures, and new approaches being adopted to develop new wireless ecosystem. Our survey includes the major progresses and outcomes from academic research as well as industrial efforts. We first start with introducing the relevant 5G use cases and general integration challenges such as handover and deployment difficulties. Then, we review the NTNs operations in mmWave and their potential for internet of things (IoT). Further, we discuss the significance of mobile edge computing (MEC) and machine learning (ML) in NTNs by reviewing the relevant research works. Furthermore, we also discuss the corresponding higher layer advancements and relevant field trials/prototyping at both academic and industrial levels. Finally, we identify and review 6G and beyond application scenarios, novel architectures, technological enablers, and higher layer aspects pertinent to NTNs integration.

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Networking Challenges for Non-Terrestrial Networks Exploitation in 5G

Cited by 33 publications

References 17 publications

Exploring Machine Learning for Classification of QUIC Flows over Satellite

Exploring Machine Learning for Classification of QUIC Flows over Satellite

Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

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