Resource Allocation Modeling for Fine-Granular Network Slicing in Beyond 5G Systems

Shu, Zhaogang; Taleb, Tarik; Song, JaeSeung

doi:10.1587/transcom.2021wwi0002

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…The goal of mULC is to provide ultra-reliable and lowlatency connectivity for enormous M2M communications in the IoT sector. AI-RAN allows for the effective administration of a large number of devices, network resources, and data traffic patterns [141]. To provide reliable and low-latency connectivity for a large number of IoT devices, researchers are studying AI-driven solutions for intelligent device authentication, automated anomaly detection, and dynamic spectrum allocation [142].…”

Section: ) Massive Ultra-reliable Low Latency Communication (Mulc)mentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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6G is a next-generation cellular communication technology that builds up on existing 5G networks which are currently rolled out worldwide. Through incorporation of artificial intelligence (AI) and machine learning (ML), the core 5G network is advanced into an intelligent 6G network. The 6G Artificial Intelligence Radio Access Network (AI-RAN) is anticipated to offer advanced features like reduced latency, improved bandwidth, data rates and coverage. Furthermore, AI-RAN is expected to support complex use cases such as extreme connectivity, multi-user communications and dynamic spectrum access. This paper provides a detailed survey and thorough assessment of AI-RAN's vision and state-of-the-art challenges. We first present a concise introduction to 6G AI-RAN followed by background information on the current 5G RAN and its challenges that must be overcome to implement 6G AI-RAN. The paper then examines trending research issues in AI-RAN, i.e., challenges related to spectrum allocation, network architecture, and resource management. We discuss the methods to overcome these challenges which include the adoption of advanced machine learning and edge computing technologies to boost the performance of 6G AI-RAN. We conclude by stating open research directions.

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Section: ) Massive Ultra-reliable Low Latency Communication (Mulc)mentioning

confidence: 99%

AI-RAN in 6G Networks: State-of-the-Art and Challenges

Khan,

Schmid

2024

IEEE Open J. Commun. Soc.

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“…To cope with these challenges, network slicing, which is empowered by the emerging software-defined networking (SDN) and network function virtualization (NFV) technologies, aims at building multiple virtual logically independent networks by constructing flexible and highly adaptable communication networks. Network slicing has achieved great progress in supporting specific demands of customers [4], [5], industries [6], and emerging applications [7]- [9] and is witnessed as a key technology for supporting specific demands in 6G networks.…”

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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“…The fifth generation (5G) of wireless communication promises to fulfil several new service and enterprise requirements, such as ultra-high speed, low latency, and multiple simultaneous connections. Metropolitan area networks (MANs) are expected to be the key to implementing such 5G services [1]- [4] and will become more important in the forthcoming "Beyond 5G" (B5G) era [5]. The research and development of network technologies and systems aiming for B5G must be conducted using appropriate MAN mod-Fig.…”

Section: Introductionmentioning

confidence: 99%

Metropolitan Area Network Model Design Using Regional Railways Information for Beyond 5G Research

Tachibana

Hirota²,

SUZUKI

et al. 2023

IEICE Trans. Commun.

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To accelerate research on Beyond 5G (B5G) technologies in Japan, we propose an algorithm that designs mesh-type metropolitan area network (MAN) models based on a priori Japanese regional railway information, because ground-truth communication network information is unavailable. Instead, we use the information of regional railways, which is expected to express the necessary geometric structure of our metropolitan cities while remaining strongly correlated with their population densities and demographic variations. We provide an additional compression algorithm for use in reducing a small-scale network model from the original MAN model designed using the proposed algorithm. Two Tokyo MAN models are created, and we provide day and night variants for each while highlighting the number of passengers alighting/boarding at each station and the respective population densities. The validity of the proposed algorithm is verified through comparisons with the Japan Photonic Network model and another model designed using the communication network information, which is not ground-truth. Comparison results show that our proposed algorithm is effective for designing MAN models and that our result provides a valid Tokyo MAN model.

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Resource Allocation Modeling for Fine-Granular Network Slicing in Beyond 5G Systems

Cited by 7 publications

References 38 publications

AI-RAN in 6G Networks: State-of-the-Art and Challenges

AI-RAN in 6G Networks: State-of-the-Art and Challenges

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

Metropolitan Area Network Model Design Using Regional Railways Information for Beyond 5G Research

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