5G Wireless Transport in a PON Context: An Overview

Wey, Jun Shan; Luo, Yuanqiu; Pfeiffer, Thomas

doi:10.1109/mcomstd.001.1900043

Cited by 39 publications

(19 citation statements)

References 2 publications

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“…These recently introduced functional split options are: (a) option #1 between the vCU and vDU; and (b) option #6, option #7-1, option #7-2a, option #7-2, and option #7-3 between the vDU and RU. These split options are analyzed and compared in [20], [21], [27], [72], [73]. In this article, our goal is not to discuss the distribution of gNB functionalities, their respective architectures, and their implications on the performance of RAN slices.…”

Section: B the Network Function Layermentioning

confidence: 99%

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Habibi

Yousaf²,

Schotten

2022

IEEE Open J. Commun. Soc.

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The mapping of a virtual network service onto a physical network infrastructure is a challenging task due to the joint allocation of virtual resources across nodes and links, the diverse technical requirements of end-users, the coordination between multiple host domains, and others. This issue is exacerbated further by the extension of virtualization to the next-generation radio access network (NG-RAN) architecture and the provisioning of radio access network (RAN) slicing. To that end, this article focuses on the mapping problem of the virtual network functions (VNFs), as well as their internal and external virtual links (VLs), of a RAN slice subnet onto intelligent points of presence (I-PoPs) and transport networks in the NG-RAN architecture. In this context, in contrast to the majority of the state-of-the-art proposals, which frequently fail to achieve performance objectives and neglect resource allocation constraints, this article introduces automation and intelligence at an architectural level to map VNFs and VLs onto their corresponding physical nodes and links, with the goal of achieving superior efficiency in virtual resource utilization while granting the performance of a RAN slice subnet. Benefiting from a top-down approach, the key contributions of this article are: (i) to extend the architectural framework of network slicing towards the NG-RAN architecture and provide a comprehensive overview and critical analysis of the components and functionalities of a RAN slice subnet; (ii) to integrate the Experiential Network Intelligence (ENI) framework into a joint architecture of the network functions virtualization-management and orchestration (NFV-MANO), Third Generation Partnership Project-network slicing management system (3GPP-NSMS), and I-PoPs in order to render automation and intelligence to the management and orchestration aspects of a RAN slice subnet in the NG-RAN architecture; and (iii) to propose a learning-assisted architectural solution for mapping the VNFs, as well as their internal and external VLs, of a RAN slice subnet onto the underlying I-PoPs and transport networks.

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Section: B the Network Function Layermentioning

confidence: 99%

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Habibi

Yousaf²,

Schotten

2022

IEEE Open J. Commun. Soc.

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“…There are many related works focused on shortening the length of legacy SR-DBA cycle. Tashiro et.al proposed and demonstrated a novel DBA scheme called cooperative DBA (CO-DBA) [6], and it is soon be standardized by ITU-T [7,8]. Besides the ITU-T, the O-RAN alliance is working on the next generation of RAN equipment, which specifies the interface between CU/DU and transport network (e.g., PON), called cooperative transport interface (CTI) [9].…”

Section: A Latency Reduction On a Tdm-pon Based Fronthaulmentioning

confidence: 99%

Low Latency DWBA Scheme for Mini-Slot Based 5G new Radio in a Fixed and Mobile Converged TWDM-PON

Zhang

Liu

Liang

et al. 2022

J. Lightwave Technol.

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Cooperative dynamic bandwidth allocation (CO-DBA) enables time division multiplexed passive optical network (TDM-PON) to become a possible solution for mobile fronthaul. Meanwhile, status-report DBA (SR-DBA) still retains its advantage on bandwidth efficiency for conventional PON services.The collaboration of CO-and SR-DBA is an enabling approach for fixed-mobile convergence into a single PON infrastructure. However, a resource allocation conflict between CO-and SR-DBA is to arise when using an emerging 5G new radio interface based on the mini-slot. This conflict will cause a large delay that can not meet the time-sensitive fronthaul traffic requirement. In this study, we consider a time and wavelength multiplexed PON (TWDM-PON) for fixed and mobile services accommodation because of its capacity and flexibility. A novel DWBA scheme is proposed by means of wavelength and bandwidth preemption (WBP) mechanism. The basic rule of WBP is that a time-sensitive traffic can preempt the wavelength and bandwidth allocated to the timetolerent traffic when a conflict happens. We compare it with an existing integrated CO-and SR-DWBA scheme without preemption under various situations. Also, we evaluate the performance of the proposed DWBA with different preemption policies.

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“…However, the cognition is not only complicated but also significantly correlated. For example, cognition of user includes both per-user level (mobile user viewpoint) and aggregate level traffic (mobile operator viewpoint) [12]. Per-user level traffic is related to user preference, mobility, and varying traffic demands.…”

Section: A Complexity and Correlation In Cognitionmentioning

confidence: 99%

Artificial Intelligence Powered Mobile Networks: From Cognition to Decision

Luo¹,

Yuan²,

Li³

et al. 2021

Preprint

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Mobile networks (MN) are anticipated to provide unprecedented opportunities to enable a new world of connected experiences and radically shift the way people interact with everything. MN are becoming more and more complex, driven by ever-increasingly complicated configuration issues and blossoming new service requirements. This complexity poses significant challenges in deployment, management, operation, optimization, and maintenance, since they require a complete understanding and cognition of MN. Artificial intelligence (AI), which deals with the simulation of intelligent behavior in computers, has demonstrated enormous success in many application domains, suggesting its potential in cognizing the state of MN and making intelligent decisions. In this paper, we first propose an AI-powered mobile network architecture and discuss challenges in terms of cognition complexity, decisions with high-dimensional action space, and self-adaption to system dynamics. Then, potential solutions that are associated with AI are discussed. Finally, we propose a deep learning approach that directly maps the state of MN to perceived QoS, integrating cognition with the decision. Our proposed approach helps operators in making more intelligent decisions to guarantee QoS. Meanwhile, the effectiveness and advantages of our proposed approach are demonstrated on a real-world dataset, involving 31261 users over 77 stations within 5 days.

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5G Wireless Transport in a PON Context: An Overview

Cited by 39 publications

References 2 publications

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Mapping the VNFs and VLs of a RAN Slice Onto Intelligent PoPs in Beyond 5G Mobile Networks

Low Latency DWBA Scheme for Mini-Slot Based 5G new Radio in a Fixed and Mobile Converged TWDM-PON

Artificial Intelligence Powered Mobile Networks: From Cognition to Decision

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