User-Based Quality of Service Aware Multi-Cell Radio Access Network Slicing

Papa, Arled; Jano, Alba; Ayvasik, Serkut; Ayan, Onur; Gürsu, H. Murat; Kellerer, Wolfgang

doi:10.1109/tnsm.2021.3122230

Cited by 19 publications

(16 citation statements)

References 42 publications

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“…3, excluding the backbone and core network details, is composed of the components of the 5G core, RAN testbed(s) and internal networks at both ends of Munich and Prague. Primarily, our testbed is developed using OpenAirInterface (OAI) [15] as a baseline and enhanced features in [16], [17]. Currently, our 5G-OCTOPUS testbeds governs the following features:…”

Section: B Network Architecture and Featuresmentioning

confidence: 99%

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

Babaians,

Gorla,

Ayvasik

et al. 2023

Preprint

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<p>Teleoperated robotic systems have become instrumental in advancing remote healthcare services, especially in tasks that require precision and expert oversight. The advent of cutting-edge telecommunication infrastructures, such as 5G, has amplified interest in these systems, although their full potential remains untapped. This study delves into the effectiveness of teleoperated robotic systems for medicine dispensing, comparing the performance of Wi-Fi and 5G networks in a transnational setup between two cities -- Prague and Munich. We focus on the robot's ability to accurately dispense a predefined volume of a syrup-like substance, simulating a delicate healthcare operation, under the guidance of a distant operator. Our research examines the system's holistic performance in real-world implementation across diverse scenarios, encompassing varying network states and feedback methods. Two primary feedback scenarios are considered: one incorporating real-time video streaming and another offering explicit quantitative data on the dispensed volume. Using a blend of quantitative and qualitative methods, we aim to determine the influence of network type and feedback on task efficacy and user satisfaction. This study provides insights into the potential and hurdles of deploying teleoperated robotic systems in crucial healthcare contexts, guiding future advancements in this domain, especially in scenarios, where precision and dependability are crucial.</p>

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Section: B Network Architecture and Featuresmentioning

confidence: 99%

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

Babaians,

Gorla,

Ayvasik

et al. 2023

Preprint

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“…On a related note, the authors in [16] consider the problem of resource allocation where network slices can be spread across multiple BSs. The objective is to allocate resources to maximize the overall throughput across all UEs, by guaranteeing a minimum data rate to everyone first.…”

Section: Related Workmentioning

confidence: 99%

“…The objective is to allocate resources to maximize the overall throughput across all UEs, by guaranteeing a minimum data rate to everyone first. However, the solution in [16] is based on a non-closed form approximation approach, which does not allow to see the explicit dependency of throughput on different input parameters. Also, the max-min fair resource allocation is not considered in [16].…”

Section: Related Workmentioning

confidence: 99%

“…However, the solution in [16] is based on a non-closed form approximation approach, which does not allow to see the explicit dependency of throughput on different input parameters. Also, the max-min fair resource allocation is not considered in [16]. Instead, in our work, we solve the problem over the entire network in its most general form for any number of UEs, BSs, and heterogeneous channel statistics while providing a closed-form expression for the max-min throughput.…”

Section: Related Workmentioning

confidence: 99%

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Max-min Fair Resource Allocation in SD-RAN

Mehmeti

Kellerer

2022

Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM Internatio

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Software-Defined Radio Access Networks (SD-RANs), introduced for the first time in 5G networks, symbolize a paradigm shift in the way the allocation of cellular network resources is performed. The main feature of SD-RAN is the possibility to decouple the control plane from the data plane, and associating the former with a controller that is located away from the Base Stations (BSs). This enables an increased flexibility in the allocation of network resources, resulting in considerable performance improvements compared to the classical pre-5G resource allocation in cellular networks. However, it is not yet clear to what extent this enhancement ranges in terms of different metrics of interest and optimization objectives. One such objective is to allocate resources so that the minimum value of the data rate in the network is maximized, i.e., to provide max-min fairness. Therefore, in this paper, we consider analytically the problem of max-min fairness in an SD-RAN environment by deriving the policy which accomplishes that along with the achievable performance, of interest to users and cellular operators. We do this for two scenarios; one in which we provide max-min fairness across all users in the network, and the other in which the goal is to provide max-min fairness in throughput across BSs, and within users of each BS. We evaluate the performance with input parameters from real data sets. Results show that the introduction of SD-RAN improves the minimum rate by up to 4× compared to the case with no SD-RAN controller. CCS CONCEPTS• Networks → Network protocol design; Network performance modeling.

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“…On the other hand, built on top of physical facilities to provide customized services to user equipment (UE), network slices not only depend on physical facilities to adjust the virtual network topology and to decide on the resource allocation strategies, but also are deeply coupled with services and UEs for ensuring service continuity [10], [11]. Under this circumstance, when the slices' serving UEs move across areas, the ongoing sessions between each pair of the moving UEs and slices may suffer a degradation in quality of service (QoS) and thus induce service continuity concern [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Federated Deep Reinforcement Learning for Prediction-Based Network Slice Mobility in 6G Mobile Networks

Ming,

Yu,

Taleb

2024

IEEE Trans. on Mobile Comput.

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Network slices are generally coupled with services and face service continuity/unavailability concerns due to the high mobility and dynamic requests from users. Network slice mobility (NSM), which considers user mobility, service migration, and resource allocation from a holistic view, is witnessed as a key technology in enabling network slices to respond quickly to service degradation. Existing studies on NSM either ignored the trigger detection before NSM decision-making or didn't consider the prediction of future system information to improve the NSM performance, and the training of deep reinforcement learning (DRL) agents also faces challenges with incomplete observations. To cope with these challenges, we consider that network slices migrate periodically and utilize the prediction of system information to assist NSM decision-making. The periodical NSM problem is further transformed into a Markov decision process, and we creatively propose a prediction-based federated DRL framework to solve it. Particularly, the learning processes of the prediction model and DRL agents are performed in a federated learning paradigm. Based on extensive experiments, simulation results demonstrate that the proposed scheme outperforms the considered baseline schemes in improving long-term profit, reducing communication overhead, and saving transmission time.

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User-Based Quality of Service Aware Multi-Cell Radio Access Network Slicing

Cited by 19 publications

References 42 publications

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

OCTOPUS: Optimized Cross-border TeleOperated Medicine Pouring Using NextGen Seamless Communication Networks

Max-min Fair Resource Allocation in SD-RAN

Federated Deep Reinforcement Learning for Prediction-Based Network Slice Mobility in 6G Mobile Networks

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