| 1. |
- Alay, Özgü, et al.
(författare)
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Monitoring and Analytics (Release B)
- 2021
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Rapport (refereegranskat)abstract
- This document describes the design and implementation of the 5GENESIS Monitoring & Analytics (M&A) framework in its Release B, developed within Task T3.3 of the project work plan. M&A Release B leverages and extends M&A Release A, which has been documented in the previous Deliverable D3.5 [1]. In particular, we present new features and enhancements introduced in this new Release compared to the Release A. We also report some examples of usage of the M&A framework, in order to showcase its integrated in the 5GENESIS Reference Architecture.
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| 2. |
- Caso, Giuseppe, et al.
(författare)
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Empirical Models for NB-IoT Path Loss in an Urban Scenario
- 2021
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Ingår i: IEEE Internet of Things Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4662. ; 8:17, s. 13774-13788
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Tidskriftsartikel (refereegranskat)abstract
- The lack of publicly available large scale measurements has hindered the derivation of empirical path loss (PL) models for Narrowband Internet of Things (NB-IoT). Therefore, simulation-based investigations currently rely on models conceived for other cellular technologies, which are characterized, however, by different available bandwidth, carrier frequency, and infrastructure deployment, among others. In this paper, we take advantage of data from a large scale measurement campaign in the city of Oslo, Norway, to provide the first empirical characterization of NB-IoT PL in an urban scenario. For the PL average term, we characterize Alpha-Beta-Gamma (ABG) and Close-In (CI) models. By analyzing multiple NBIoT cells, we propose a statistical PL characterization, i.e., the model parameters are not set to a single, constant value across cells, but are randomly extracted from well-known distributions. Similarly, we define the PL shadowing distribution, correlation over distance, and inter-site correlation. Finally, we give initial insights on outdoor-to-indoor propagation, using measurements up to deep indoor scenarios. The proposed models improve PL estimation accuracy compared to the ones currently adopted in NB-IoT investigations, enabling more realistic simulations of urban scenarios similar to the sites covered by our measurements.
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| 3. |
- Caso, Giuseppe, et al.
(författare)
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NB-IoT Random Access : Data-driven Analysis and ML-based Enhancements
- 2021
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 8:14, s. 11384-11399
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Tidskriftsartikel (refereegranskat)abstract
- In the context of massive Machine Type Communications (mMTC), the Narrowband Internet of Things (NB-IoT) technology is envisioned to efficiently and reliably deal with massive device connectivity. Hence, it relies on a tailored Random Access (RA) procedure, for which theoretical and empirical analyses are needed for a better understanding and further improvements. This paper presents the first data-driven analysis of NB-IoT RA, exploiting a large scale measurement campaign. We show how the RA procedure and performance are affected by network deployment, radio coverage, and operators’ configurations, thus complementing simulation-based investigations, mostly focused on massive connectivity aspects. Comparison with the performance requirements reveals the need for procedure enhancements. Hence, we propose a Machine Learning (ML) approach, and show that RA outcomes are predictable with good accuracy by observing radio conditions. We embed the outcome prediction in a RA enhanced scheme, and show that optimized configurations enable a power consumption reduction of at least 50%. We also make our dataset available for further exploration, toward the discovery of new insights and research perspectives.
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| 4. |
- Caso, Giuseppe, et al.
(författare)
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User-Centric Radio Access Technology Selection : A Survey of Game Theory Models and Multi-Agent Learning Algorithms
- 2021
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Ingår i: IEEE Access. - : Institute of Electrical and Electronics Engineers (IEEE). - 2169-3536. ; 9, s. 84417-84464
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Tidskriftsartikel (refereegranskat)abstract
- User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.
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| 5. |
- Rabitsch, Alexander, et al.
(författare)
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Extending Network Slice Management to the End-host
- 2021
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Ingår i: ACM SIGCOMM 2021 Workshop on 5G Measurements, Modeling, and Use Cases (5G-MeMU), August 23, 2021.. - 1601 Broadway, 10th Floor New York, NY 10019-7434 : ACM Digital Library. ; , s. 20-26
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Konferensbidrag (refereegranskat)abstract
- The network slicing concept of 5G aims to provide the flexibility and scalability required to support a wide array of vertical services. To coordinate the coexistence of network slices, and to guarantee that the required resources are available for each one of them, the 5G core employs a slicing management entity, a slice manager. In this paper, we propose an architecture where the network slicing concept is extended beyond the core and access networks to also include the configuration of the UE’s network stack.We exploit the slice manager’s global view on the network to feed fine-grained information on slice configuration, health, and status to the UE. This information, together with local policies on the UE, is then used to dynamically create services tailored to the requirements of individual applications. We implement this architecture in a 5G testbed, and show how it can be leveraged in order to enable optimized services through dynamic network protocol configuration, application-to-slice mapping, and network protocol selection.
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| 6. |
- Wu, Hongjia, et al.
(författare)
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A Survey on Multipath Transport Protocols Towards 5G Access Traffic Steering, Switching and Splitting
- 2021
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Ingår i: IEEE Access. - : IEEE. - 2169-3536. ; 9, s. 164417-164439
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Tidskriftsartikel (refereegranskat)abstract
- The fifth generation (5G) cellular network aims at providing very high data rates, ultra reliable low latency communications, and a vast increase of connection density. As one of the design trends towards these objectives, 5G exploits multi-connectivity, i.e., the concurrent use of multiple access networks. The Access Traffic Steering, Switching, and Splitting (ATSSS) architecture has recently been proposed to enable 5G multi-connectivity, and multipath transport protocols have emerged as a key ATSSS technology enabler. Within this context, this survey presents a detailed review of multipath transport protocols, identifies their existing and potential exploitation in ATSSS, and suggests their applicability for enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC) services. To this end, we first review 5G background and current standardization activities around multi-connectivity and the ATSSS architecture. We then provide an in-depth review of multipath transport protocols, covering four core functionalities, i.e., path management, scheduling, congestion control, and reliable transfer. Based on the reviewed literature, we further discuss the integration of multipath transport into ATSSS to achieve eMBB and URLLC service requirements. Finally, we also point out major open research issues and discuss possible future directions.
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| 7. |
- Wu, Hongjia, et al.
(författare)
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Multipath Scheduling for 5G Networks : Evaluation and Outlook
- 2021
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Ingår i: IEEE Communications Magazine. - : Institute of Electrical and Electronics Engineers (IEEE). - 0163-6804 .- 1558-1896. ; 59:4, s. 44-50
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Tidskriftsartikel (refereegranskat)abstract
- The fifth generation (5G) of cellular networks aims at providing very high data rates, ultra-reliable low latency, and massive connection density. As one of the fundamental design trends toward these objectives, 5G exploits multi-connectivity (i.e., the concurrent use of multiple access networks), where multipath transport protocols have emerged as key technology enablers. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths and has a critical impact on the protocol performance. Within this context, we present in this article the first empirical evaluation of state-of-the-art multipath schedulers based on real 5G data, for both static and mobile scenarios. Furthermore, we introduce M-Peekaboo, which builds on a state-of-the-art learning-based multipath scheduler and extends its usage to 5G networks. Our results illustrate the benefits of employing a learning-based multipath scheduler for 5G networks and motivate further studies of advanced learning schemes that can adapt more quickly to the path conditions, and take into account the emerging features and requirements of 5G and beyond networks.
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