A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks

Larsen, Line M. P.; Checko, Aleksandra; Christiansen, Henrik Lehrmann

doi:10.1109/comst.2018.2868805

Cited by 316 publications

(247 citation statements)

References 106 publications

(307 reference statements)

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“…If mode x is selected in the n-th block of epoch m, we have α m,n,x > 0, with the complementary slackness condition η m,n,x α m,n,x = 0, we have η m,n,x = 0. According to (13), let ∂L ∂αm,n,x = 0,…”

Section: Maximizing the Throughputmentioning

confidence: 99%

Flexible Functional Split and Power Control for Energy Harvesting Cloud Radio Access Networks

Wang

Zhou

2020

IEEE Trans. Wireless Commun.

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Functional split is a promising technique to flexibly balance the processing cost at remote ends and the fronthaul rate in cloud radio access networks (C-RAN). By harvesting renewable energy, remote radio units (RRUs) can save grid power and be flexibly deployed. However, the randomness of energy arrival poses a major design challenge. To maximize the throughput under the average fronthaul rate constraint in C-RAN with renewable powered RRUs, we first study the offline problem of selecting the optimal functional split modes and the corresponding durations, jointly with the transmission power.We find that between successive energy arrivals, at most two functional split modes should be selected.Then the optimal online problem is formulated as an Markov decision process (MDP). To deal with the curse of dimensionality of solving MDP, we further analyze the special case with one instance of energy arrival and two candidate functional split modes as inspired by the offline solution, and then a heuristic online policy is proposed. Numerical results show that with flexible functional split, the throughput can be significantly improved compared with fixed functional split. Also, the proposed heuristic online policy has similar performance with the optimal online one, as validated by simulations. Index TermsFunctional split, energy harvesting, cloud radio access network (C-RAN), fronthaul, Markov decision process (MDP). PDCPHigh RLC Low RLC High MAC Low MAC High PHY Low PHY Low PHY High PHY Low MAC High MAC Low RLC High RLC PDCP Uplink Downlink BBU RRU RF RF Fig. 1. Illustration of baseband functions with multiple candidate split modes. I. INTRODUCTION Cloud radio access network (C-RAN) [2], which centralizes the baseband functions at the baseband units (BBUs), can efficiently reduce the complexity of the remote radio units (RRUs), and thus the operation and deployment costs. Centralized baseband processing also enables efficient cooperative signal processing to increase the network capacity. In C-RAN, the fronthaul network transports the baseband signals between the BBUs and the RRUs. However, for fully centralized C-RAN, i.e., all baseband functions are centralized at the BBUs, the fronthaul rate requirement is high, which poses a major design challenge on C-RAN. For example, in a single 20MHz LTE antenna-carrier system, 1Gbps fronthaul rate is required with the standard CPRI interface [3]. To support massive MIMO and other emerging technologies, the required fronthaul rate will be too high to bear. Different from fully centralized C-RAN, by placing some baseband and network functions at RRUs, functional split is a promising technique to reduce the fronthaul rate requirement [4], [5]. There are multiple candidate functional split modes corresponding to different split points on the chain of baseband functions, as illustrated in Fig. 1. For each mode, the functions placed at the right side of the corresponding vertical dashed line are placed at the RRU, while the others are centralized at the BBU. The fronthaul rate requi...

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Section: Maximizing the Throughputmentioning

confidence: 99%

Flexible Functional Split and Power Control for Energy Harvesting Cloud Radio Access Networks

Wang

Zhou

2020

IEEE Trans. Wireless Commun.

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“…Each gNB provides NR user/control plane protocol terminations towards the User Equipments (UEs). In turn, each gNB comprises one Centralized Unit (CU), multiple Distributed Units (DUs) and multiple Radio Units (RUs) [5].…”

Section: Ng-ran Architecturementioning

confidence: 99%

Harmonizing 3GPP and NFV Description Models: Providing Customized RAN Slices in 5G Networks

Adamuz-Hinojosa

Muñoz

Ordoñez-Lucena

et al. 2019

IEEE Veh. Technol. Mag.

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Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The standardization of Radio Access Network (RAN) in mobile networks has traditionally been led by 3GPP. However, the emergence of RAN slicing has introduced new aspects that fall outside 3GPP scope. Among them, network virtualization enables the particularization of multiple RAN behaviors over a common physical infrastructure. Using Virtualized Network Functions (VNFs) that comprise customized radio functionalities, each virtualized RAN, denominated RAN slice, could meet its specific requirements. Although 3GPP specifies the description model to manage RAN slices, it can neither particularize the behavior of a RAN slice nor leverage the NFV descriptors to define how its VNFs can accommodate its spatial and temporal traffic demands. In this article, we propose a description model that harmonizes 3GPP and ETSI-NFV viewpoints to manage RAN slices. The proposed model enables the translation of RAN slice requirements into customized virtualized radio functionalities defined through NFV descriptors. To clarify this proposal, we provide an example where three RAN slices with disruptive requirements are described following our solution. Figure 1 Relationship between the 3GPP and ETSI-NFV scopes for the deployment and operation of RAN slices subnets. The aspects within the dotted box are open questions that are addressed in this article.

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“…In SDI, SDN and NFV can be leveraged to apply MKL in autonomous network management [5]. SDR provides programmability in the access nodes of RAN in which via function splitting in RAN [17] and providing a cloud close to the end users, e.g., mobile edge computing (MEC), a more flexible architecture for RAN is available to serve AI-aaS use cases. In the computation domain, based on microservices structures over containers, any AI engines can be deployed in SDI (from edge via MEC to core), or in the third parties clouds.…”

Section: Sdi Based Architecture To Serve Ai-aasmentioning

confidence: 99%

Artificial Intelligence as a Service (AI-aaS) on Software-Defined Infrastructure

Parsaeefard

Tabrizian

Leon-Garcia

2019

2019 IEEE Conference on Standards for Communications and Networking (CSCN)

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This paper investigates a paradigm for offering artificial intelligence as a service (AI-aaS) on software-defined infrastructures (SDIs). The increasing complexity of networking and computing infrastructures is already driving the introduction of automation in networking and cloud computing management systems. Here we consider how these automation mechanisms can be leveraged to offer AI-aaS. Use cases for AI-aaS are easily found in addressing smart applications in sectors such as transportation, manufacturing, energy, water, air quality, and emissions. We propose an architectural scheme based on SDIs where each AI-aaS application is comprised of a monitoring, analysis, policy, execution plus knowledge (MAPE-K) loop (MKL). Each application is composed as one or more specific service chains embedded in SDI, some of which will include a Machine Learning (ML) pipeline. Our model includes a new training plane and an AI-aaS plane to deal with the model-development and operational phases of AI applications. We also consider the role of an ML/MKL sandbox in ensuring coherency and consistency in the operation of multiple parallel MKL loops. We present experimental measurement results for three AI-aaS applications deployed on the SAVI testbed: 1. Compressing monitored data in SDI using autoencoders; 2. Traffic monitoring to allocate CPUs resources to VNFs; and 3. Highway segment classification in smart transportation.Index Terms-Artificial intelligence as a service (AI-aaS), cognitive network management, MAPE-K loop, kubernetes, Kubeflow, MEC, micro service management.

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A Survey of the Functional Splits Proposed for 5G Mobile Crosshaul Networks

Cited by 316 publications

References 106 publications

Flexible Functional Split and Power Control for Energy Harvesting Cloud Radio Access Networks

Flexible Functional Split and Power Control for Energy Harvesting Cloud Radio Access Networks

Harmonizing 3GPP and NFV Description Models: Providing Customized RAN Slices in 5G Networks

Artificial Intelligence as a Service (AI-aaS) on Software-Defined Infrastructure

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