Network Slicing in Fog Radio Access Networks: Issues and Challenges

Xiang, Haige; Zhou, Wenjiao; Daneshmand, Mahmoud; Peng, Mugen

doi:10.1109/mcom.2017.1700523

Cited by 70 publications

(38 citation statements)

References 11 publications

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“…As a cost-efficient way to support diverse use cases, network slicing has been advocated by both academia and industry [153]. The core of network slicing is to allocate appropriate resources including computing, caching, backhaul/fronthaul and radio resources on demand to guarantee the performance requirements of different slices under slice isolation constraints.…”

Section: Machine Learning Based Network Slicingmentioning

confidence: 99%

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

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493

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As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.

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Section: Machine Learning Based Network Slicingmentioning

confidence: 99%

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Sun

Peng

Zhou

et al. 2019

IEEE Commun. Surv. Tutorials

Self Cite

493

290

View full text Add to dashboard Cite

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“…Conventional approaches mainly consider inter-slice resource virtualization among tenants from the perspective of a centralized controller to achieve fairness among tenants [15][16][17][18][19][20][21]. To achieve accurate resource demand estimation and efficient resource utilization, some studies have been conducted on resource reservation for slices and intra-slice resource allocation from the perspective of each tenant [22,23].…”

Section: Motivationmentioning

confidence: 99%

A Two-Timescale Approach for Network Slicing in C-RAN

Zhang

Wong

2020

IEEE Trans. Veh. Technol.

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Network slicing is a promising technique for cloud radio access networks (C-RANs). It enables multiple tenants (i.e., service providers) to reserve resources from an infrastructure provider. However, users' mobility and traffic variation result in resource demand uncertainty for resource reservation. Meanwhile, the inaccurate channel state information (CSI) estimation may lead to difficulties in guaranteeing the quality of service (QoS). To this end, we propose a two-timescale resource management scheme for network slicing in C-RAN, aiming at maximizing the profit of a tenant, which is the difference between the revenue from its subscribers and the resource reservation cost. The proposed scheme is under a hierarchical control architecture which includes long timescale resource reservation for a slice and short timescale intra-slice resource allocation. To handle traffic variation, we utilize the statistics of users' traffic. Moreover, to guarantee the QoS under CSI uncertainty, we apply the uncertainty set of CSI for resource allocation among users. We formulate the profit maximization as a twostage stochastic programming problem. In this problem, long timescale resource reservation for a slice is performed in the first stage with only the statistical knowledge of users' traffic. Given the decision in the first stage, short timescale intra-slice resource allocation is performed in the second stage, which is adaptive to real-time user arrival and departure. To solve the problem, Abstract iv we first transform the stochastic programming problem into a deterministic optimization problem. We then introduce a maximum interference constraint and transform the QoS constraint under CSI uncertainty into linear matrix inequalities. We further apply semidefinite relaxation to transform the problem into a mixed integer nonconvex optimization problem, which can be solved by combining branch-and-bound and primal-relaxed dual techniques. Simulation results show that our proposed scheme can well adapt to traffic variation and CSI uncertainty. It obtains a higher profit when compared with several baseline schemes. v Lay Summary Network slicing is a promising technique for the fifth generation (5G) wireless systems. It allows multiple service providers to run on top of a shared physical network infrastructure. Meanwhile, cloud radio access network (C-RAN) is a centralized, cloud computing-based architecture for radio access networks to support various types of traffic demand in 5G wireless systems. However, implementing network slicing in C-RAN is faced with critical challenges due to time-varying network conditions and inaccurate knowledge of the conditions. In this thesis, to tackle the aforementioned challenges, we propose a dynamic resource management scheme for network slicing in C-RAN. Simulation results show that our proposed scheme can achieve a better performance when compared with several baseline schemes.

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“…However, current studies mainly concentrate on core networks based slicing, and the impact of the characteristics of RANs is not well considered. To get a more effective network slicing solution, the author in [19] proposes an enhanced network slicing approach in F-RANs, termed as access slicing. The proposed access slicing architecture is shown in Figure 4, where the networking strategies of three typical scenarios are demonstrated, including massive machine-type communications (mMTC), ultra-reliable MTC (uMTC), and enhanced mobile broadband (eMBB).…”

Section: Access Slicingmentioning

confidence: 99%

“…Although the literature [19] makes a big step for network slicing, several challenges exist for its implementation. First, as stated in [19], the information awareness allows the access slice orchestration layer make intelligent decisions on slice instance creation and resource management.…”

Section: Access Slicing In Hetnetsmentioning

confidence: 99%

Recent Advances of Heterogenous RadioAccess Networks: A Survey

Sun

Peng

2018

JMM

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As a promising paradigm to provide high spectral efficiency and energy efficiency, heterogenous radio access networks (HetNets) have attracted a lot of attention from both academia and industry. This paper presents a comprehensive survey of the recent advances in HetNets, including system architecture evolutions, key techniques, and open issues. The system architectures introduced include conventional HetNets, HetNets with cloud computing, and HetNets with fog computing. In addition, a novel performance metric, together with network self-organization and access slicing, is elaborated, which can help realize a cost-efficient HetNet. Moreover, the other state-of-the-art key techniques in HetNets are surveyed, including nonorthogonal multiple access, interference suppression, and channel estimation in the physical layer, the radio resource allocation in the medium access control layer, and clustering in the network layer. Given the extensiveness of the research area, future research opportunities are identified, which are related to access slicing, HetNets driven by deep learning, and so on.

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Network Slicing in Fog Radio Access Networks: Issues and Challenges

Cited by 70 publications

References 11 publications

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

A Two-Timescale Approach for Network Slicing in C-RAN

Recent Advances of Heterogenous RadioAccess Networks: A Survey

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