Sliced-RAN: Joint Slicing and Functional Split in Future 5G Radio Access Networks

Ojaghi, Behnam; Adelantado, Ferrán; Kartsakli, Elli; Antonopoulos, Angelos; Verikoukis, Christos

doi:10.1109/icc.2019.8761081

Cited by 39 publications

(29 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[18]. The authors in [19] select splits to minimize inter-cell interference; [20] and [21] consider adaptive splits; [22] optimizes the splits for RAN slicing; and our previous work [7] optimized jointly the routing and splits. This is very important as vRANs use a shared packet-based network instead of dedicated CPRI links, and we refer the reader to our pertinent feasibility study [9].…”

Section: Related Workmentioning

confidence: 99%

“…We assume that the default cost of CU-m VM instantiation is a half 14 of DU-n (a m = α n /2) [7], [16], [42] but we also explore the impact of different ratios. The cost of deploying of the CUs and its routing CU-EPC takes values in the interval ω m ∈ [15,22], where ω m ≤ ω m+1 [16]. The CU processing cost is set to b m = 0.017β n according to our measurements in [42].…”

Section: A Network Topology and Evaluation Setupmentioning

confidence: 99%

See 1 more Smart Citation

An Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks

Murti

Ayala‐Romero

García‐Saavedra³

et al. 2021

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Virtualized radio access networks (vRAN) are emerging as a key component of wireless cellular networks, and it is therefore imperative to optimize their architecture. vRANs are decentralized systems where the Base Station (BS) functions can be split between the edge Distributed Units (DUs) and Cloud computing Units (CUs); hence they have many degrees of design freedom. We propose a framework for optimizing the number and location of CUs, the function split for each BS, and the association and routing for each DU-CU pair. We combine a linearization technique with a cutting-planes method to expedite the exact problem solution. The goal is to minimize the network costs and balance them with the criterion of centralization, i.e., the number of functions placed at CUs. Using data-driven simulations we find that multi-CU vRANs achieve cost savings up to 28% and improve centralization by 77%, compared to single-CU vRANs. Interestingly, we see non-trivial trade-offs among centralization and cost, which can be aligned or conflicting based on the traffic and network parameters. Our work sheds light on the vRAN design problem from a new angle, highlights the importance of deploying multiple CUs, and offers a rigorous optimization tool for balancing costs and performance.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: A Network Topology and Evaluation Setupmentioning

confidence: 99%

An Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks

Murti

Ayala‐Romero

García‐Saavedra³

et al. 2021

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…Other works, such as [33], have also analyzed the interplay between dynamic split selection and network slicing.…”

Section: Related Workmentioning

confidence: 99%

Understanding the Performance of Flexible Functional Split in 5G vRAN Controllers: A Markov Chain-Based Model

Díez

Hervella

Agüero

2021

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

We study Flexible Functional Split functionality of 5G vRAN controllers in 5G networks. We propose an innovative model, based on a Markov Chain, which can be used to characterize their performance. We consider both infinite and finite-buffer controllers. In the former, frames would not be lost (provided the system works in a stable regime), and we thus focus on the time frames stay at the controller. For the finite-buffer controller, there might be losses, and we analyze the trade-off between time at the controller (which might hinder the stringent delay requirements of 5G services), and loss probability. Matrix-geometric techniques are used to resolve the corresponding Quasi-Birth-Death process. The validity of the proposed model is assessed by means of an extensive experiment campaign carried out over an ad-hoc eventdriven simulator, which is also used to broaden the analysis, considering different service rate distributions, as well as the variability of the studied performance indicators. The results show that the proposed model can be effectively exploited to tackle the dimensioning of these systems, as it sheds light on how their configuration impacts the expected delay and loss rate.

show abstract

“…In RAN slicing, device association and relevant resource allocation are fundamentally distinct from that in conventional mobile networks because of the introduction of NS [4], [5]. On one hand, NSs are logically virtualized and isolated over shared physical networks [4], [5]. Thus, both physical and virtual resource, e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, both physical and virtual resource, e.g. computing, network, storage, radio, access hardware, and virtual network functions, should be considered to form a function chain for a specific service [4]. On the other hand, to meet the service requirements, a device needs to select an appropriate NS which may cover multiple access points (APs), i.e., base stations (BSs) [5].…”

Section: Introductionmentioning

confidence: 99%

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

Liu

Feng

Sun

et al. 2020

IEEE Trans. Veh. Technol.

View full text Add to dashboard Cite

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.

show abstract

Sliced-RAN: Joint Slicing and Functional Split in Future 5G Radio Access Networks

Cited by 39 publications

References 12 publications

An Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks

An Optimal Deployment Framework for Multi-Cloud Virtualized Radio Access Networks

Understanding the Performance of Flexible Functional Split in 5G vRAN Controllers: A Markov Chain-Based Model

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

Contact Info

Product

Resources

About