Resource Allocation for Network Slicing in 5G Telecommunication Networks: A Survey of Principles and Models

Su, Ruoyu; Zhang, Dengyin; Venkatesan, R.; Gong, Zizheng; Li, Cheng; Ding, Fei; Jiang, Fan; Zhu, Zhaohui

doi:10.1109/mnet.2019.1900024

Cited by 147 publications

(96 citation statements)

References 15 publications

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“…However, considering that endto-end slices span both CN and RAN, in order to improve the efficiency and resource utilization of the virtualized networks, RAN slicing should also be considered to provide specific services for smart devices through end-to-end NSs. The authors of [19]- [23] pointed out that device association is one of the key issues in virtualized networks since that device association determining whether a device is associated with a certain NS via a specific BS, plays a crucial role for load balancing, radio spectrum efficiency, and network efficiency [20]. Moreover, device association in sliced mobile networks is fundamentally distinct from that in conventional mobile networks because of the device-BS-NS three layer association relationship.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, device association in sliced mobile networks is fundamentally distinct from that in conventional mobile networks because of the device-BS-NS three layer association relationship. Thus existing access/handoff control schemes for traditional mobile network cannot be applicable to virtualized mobile networks [20], [21]. Specifically, a joint optimization of NS and BS selection for a device with specific QoS requirements should be addressed [19]- [23].…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the handoff under device-BS-NS three layer relationship is different from traditional reference signal received power (RSRP)-based handoff mechanisms. Both the handoff types (i.e., switching NS only, switching BS only, switching NS and BS) and the RSRP of BS should be taken into account to guarantee the service quality [20], [21], [23].…”

Section: Related Workmentioning

confidence: 99%

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Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Liu

Feng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-the-art solutions.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Liu

Feng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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“…A more general survey on network slicing for wireless networks (not specific to 5G wireless networks) has been presented by Richart et al [71]. The surveys [72]- [75] have discussed network slicing and the management of resources in the context of 5G based on both SDN and NFV.…”

Section: ) Software Defined Networking (Sdn) For Nfsmentioning

confidence: 99%

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

2020

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In order to facilitate flexible network service virtualization and migration, network functions (NFs) are increasingly executed by software modules as so-called "softwarized NFs" on General-Purpose Computing (GPC) platforms and infrastructures. GPC platforms are not specifically designed to efficiently execute NFs with their typically intense Input/Output (I/O) demands. Recently, numerous hardwarebased accelerations have been developed to augment GPC platforms and infrastructures, e.g., the central processing unit (CPU) and memory, to efficiently execute NFs. This article comprehensively surveys hardware-accelerated platforms and infrastructures for executing softwarized NFs. This survey covers both commercial products, which we consider to be enabling technologies, as well as relevant research studies. We have organized the survey into the main categories of enabling technologies and research studies on hardware accelerations for the CPU, the memory, and the interconnects (e.g., between CPU and memory), as well as custom and dedicated hardware accelerators (that are embedded on the platforms); furthermore, we survey hardware-accelerated infrastructures that connect GPC platforms to networks (e.g., smart network interface cards). We find that the CPU hardware accelerations have mainly focused on extended instruction sets and CPU clock adjustments, as well as cache coherency. Hardware accelerated interconnects have been developed for on-chip and chip-to-chip connections. Our comprehensive up-to-date survey identifies the main trade-offs and limitations of the existing hardware-accelerated platforms and infrastructures for NFs and outlines directions for future research.

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“…Creating an independent virtualized end-to-end (E2E) network involves the configuration of the radio access network (RAN), transport, and core network [7]. However, the complexity of the configuration in the RAN is greater due to difficulties in partitioning radio resources and virtualizing functionalities with tight latency requirements [8]- [9]. On the one hand, slices cannot interfere with each other to ensure isolation.…”

Section: Introductionmentioning

confidence: 99%

Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond

Muñoz¹,

Adamuz-Hinojosa²,

Navarro-Ortiz³

et al. 2020

IEEE Access

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The new fifth generation (5G) era has transformed previous mobile generations into fast, smart networks that will be more responsive and customizable. With network slicing, 5G networks can be dynamically adapted to the different needs of specific vertical industries. This capability has opened the opportunity to new business models whereby infrastructure owners can monetize their investment by leasing resources to third parties (i.e. tenants). In this respect, a challenging task for the owner of the radio access network infrastructure (i.e. the network provider) is the spectrum planning of multi-tenant scenarios. This paper proposes different alternatives of hiring capacity to the provider as well as a set of spectrum planning strategies, each giving a certain degree of flexibility to allocate resources per tenant. These strategies are evaluated in a 5G small cell multi-tenant network through snapshot-based simulations. The performance of the strategies is assessed in terms of scalability, spectrum isolation, utilization and efficiency. INDEX TERMS 5G networks, multi-tenancy, new radio, RAN slicing, small cells, spectrum planning.

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Resource Allocation for Network Slicing in 5G Telecommunication Networks: A Survey of Principles and Models

Cited by 147 publications

References 15 publications

Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Device Association for RAN Slicing Based on Hybrid Federated Deep Reinforcement Learning

Hardware-Accelerated Platforms and Infrastructures for Network Functions: A Survey of Enabling Technologies and Research Studies

Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond

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