Ensemble Learning Method-Based Slice Admission Control for Adaptive RAN

Moon, Seung Ii; Hirayama, Haruhisa; Tsukamoto, Yu; Nanba, Shinobu; Shinbo, Hiroyuki

doi:10.1109/gcwkshps50303.2020.9367536

Cited by 12 publications

(10 citation statements)

References 10 publications

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“…Article [11] has addressed the opportunities and challenges of RAN slicing using ML-based techniques. An ensemble learning method-based SAC for adaptive RAN is proposed in [12]. Its objective is to support communication services.…”

Section: Related Workmentioning

confidence: 99%

Approximate Q-learning-based (AQL) network slicing in mobile edge-cloud for delay-sensitive services

Khani,

Jamali,

Sohrabi

2023

J Supercomput

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Network slicing (NS) technology promises to provide a variety of services based on diverse latencysensitive over shared infrastructure in Mobile Edge-Cloud (MEC) by creating customized slices for each application. However, to process users' dynamic slice requests, the infrastructure provider (InP) must be the online slice acceptance check and scaled if required. Based on a business model, network revenue is dependent on the acceptance of slices and the provision of infrastructure for them. If an InP does not provide more resources for an active slice, the network is penalized and its revenues are degraded. A proper solution to the aforementioned problem is to use reinforcement learning methods. But most of these methods have challenges in continuous spaces. This work presents a reinforcement learning-based method called approximate Q-learning (AQL) to intelligently slice acceptance control (SAC) to maximize utility in MEC for latency-sensitive services. The core idea of AQL is based on Q-learning, so we have developed some of its functions to adapt to a large area of spaces and actions. We have evaluated the performance of AQL in terms of coverage, cumulative rewards, resource utilization, and revenue. The results show the proposed approach has an acceptable performance.

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Section: Related Workmentioning

confidence: 99%

Approximate Q-learning-based (AQL) network slicing in mobile edge-cloud for delay-sensitive services

Khani,

Jamali,

Sohrabi

2023

J Supercomput

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“…Similarly, the authors in [121] address the end-user admission problem, and rely on a DRL approach to solve it. In particular, the ensemble learning method (ELM), which exploits the benefits of SPG and Approximation Framework (AF), is used.…”

Section: ) End-user Admission Controlmentioning

confidence: 99%

Machine Learning in Network Slicing—A Survey

Phyu¹,

Naboulsi²,

Stanica

2023

IEEE Access

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5G and beyond networks are expected to support a wide range of services, with highly diverse requirements. Yet, the traditional "one-size-fits-all" network architecture lacks the flexibility to accommodate these services. In this respect, network slicing has been introduced as a promising paradigm for 5G and beyond networks, supporting not only traditional mobile services, but also vertical industries services, with very heterogeneous requirements. Along with its benefits, the practical implementation of network slicing brings a lot of challenges. Thanks to the recent advances in machine learning (ML), some of these challenges have been addressed. In particular, the application of ML approaches is enabling the autonomous management of resources in the network slicing paradigm. Accordingly, this paper presents a comprehensive survey on contributions on ML in network slicing, identifying major categories and subcategories in the literature. Lessons learned are also presented and open research challenges are discussed, together with potential solutions.

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“…A policy for resource allocation according to controlling slice admission was discussed in 5G RAN slicing environments. In [25], an ensemble learning method is used to reduce learning time and improve performance for the adaptive RAN. Due to the diverse QoS requirements in IoT network, different QoS requirements (i.e., latency and data rate) should be considered when performing resource allocation.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the diverse QoS requirements in IoT network, different QoS requirements (i.e., latency and data rate) should be considered when performing resource allocation. However, the resource allocation policies in [22][23][24][25] ignored the heterogeneous QoS requirements in the IoT network.…”

Section: Related Workmentioning

confidence: 99%

Service Provisioning in Sliced Cloud Radio Access Networks

Zou

Min

Cui

et al. 2022

Wireless Communications and Mobile Computing

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Network slicing- (NS-) based cloud radio access networks (C-RANs) have emerged as a key paradigm to support various novel applications in 5G and beyond networks. However, it is still a challenge to allocate resources efficiently due to heterogeneous quality of service (QoS) requirements of diverse services as well as competition among different network slices. In this paper, we consider a service provisioning allocation framework to guarantee resource utilization while ensuring the QoS of users. Specifically, an inter/intraslice bandwidth optimization strategy is developed to maximize the revenue of the system with multiple network slices. The proposed strategy is hierarchically structured, which decomposes into network-level slicing and packet scheduling level slicing. At the network level, resources are allocated to each slice. At the packet scheduling level, each slice allocates physical resource blocks (PRBs) among users associated with the slice. Numerical results show that the proposed strategy can effectively improve the revenue of the system while guaranteeing heterogeneous QoS requirements. For example, the revenue of the proposed strategy is 21% higher than that of the average allocation strategy.

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Ensemble Learning Method-Based Slice Admission Control for Adaptive RAN

Cited by 12 publications

References 10 publications

Approximate Q-learning-based (AQL) network slicing in mobile edge-cloud for delay-sensitive services

Approximate Q-learning-based (AQL) network slicing in mobile edge-cloud for delay-sensitive services

Machine Learning in Network Slicing—A Survey

Service Provisioning in Sliced Cloud Radio Access Networks

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