A Survey of Mobility Management as a Service in Real-Time Inter/Intra Slice Control

Mohammedali, Noor Abdalkarem; Kanakis, Triantafyllos; Agyeman, Michael Opoku; Al-Sherbaz, Ali

doi:10.1109/access.2021.3074024

Cited by 12 publications

(8 citation statements)

References 95 publications

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“…Computing B TC r,w,s and B TC c,w,s is accurate (if one compares the analytical results with the corresponding simulation results) but complex especially for a system of many RRHs that have large capacities and serve many service classes. This is because it is required to enumerate/process Ω so as to compute the normalization constant G of ( 4) and determine all blocking states required in ( 6) and (7). For this reason, the BF method can be adopted only for small C-RAN examples (of tutorial nature).…”

Section: Tc Probabilities Via the Proposed Bf Methodsmentioning

confidence: 99%

“…Among other advantages of BBU pooling, we mention the possibility of many operators sharing the same BBU pool as well as the more convenient BBU addition/upgrade compared to traditional RAN [5,6]. To benefit from network function virtualization (NFV) [7], we adopt the case of virtualized BBU computational resources (V-BBU) connected to the RRHs assuming a large capacity and low-latency fronthaul, under the common public radio interface (CPRI) [8].…”

Section: Introductionmentioning

confidence: 99%

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Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

et al. 2021

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In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units and are separated from the remote radio heads (RRHs). Based on their radio capacity, the RRHs may form one or many clusters: a single cluster when all RRHs have the same capacity and multi-clusters where RRHs of the same radio capacity are grouped in the same cluster. Each RRH services the so-called multiservice traffic, i.e., calls from many service classes with various radio and computational resource requirements. Calls arrive in the RRHs according to a quasi-random process. This means that new calls are generated by a finite number of mobile users. Arriving calls require simultaneously computational and radio resource units in order to be accepted in the system, i.e., in the serving RRH. If their requirements are met, then these calls are served in the (serving) RRH for a service time which is generally distributed. Otherwise, call blocking occurs. We start with the single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time congestion probabilities via a convolution algorithm whose accuracy is validated with the aid of simulation. Furthermore, the previous model is generalized to include the more complex case of more than one clusters.

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Section: Tc Probabilities Via the Proposed Bf Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

et al. 2021

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“…The Proposed E2E slice model builds on top of Free5gc open-source code for the 5G core and another open-source code called UERANSIM for the UE and RAN [13]. The implementation of the S-NSSAI was done based on the 3GPP TS standard and as explained in [9].…”

Section: The Proposed E2e Slice Model Using the Current 5g Test-bedmentioning

confidence: 99%

“…This paper is a continue work for our overview in [9], we have surveyed all softwarization and virtualization techniques that could be used to implement the network slicing in the future networks. Furthermore, we considered the traffic classification for inter/intra slice depending on the use cases.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation for End-to-End Slice Management in 5G/B5G Cellular Networks

Mohammedali

Kanakis

Al-Sherbaz

et al. 2022

2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)

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End-to-End (E2E) virtual networks represent a key technology in future cellular networks. Generally, the E2E connection means each slice has an independent part of the RAN, User Plane Function (UPF) and the 5G Core. Within each slice, a subscriber may have one or more Quality of Service (QoS) flows. These flows only exist within the slices. According to the 3G Partnership Project (3GPP) Technical Specification (TS), it could be at most eight Single Network Slice Selection Assistance Informations (S-NSSAIs) in the Allowed list. Requested NSSAIs sent in signalling messages; registration request, accept and respectively; between the user and the network. These messages allow the network to select the serving Access and Mobility Management Function (AMF), network slices and Network Slice Instances (NSIs)for the user. The research idea is to improve the Quality of Service (QoS) and the Quality of Experience (QoE) for the user when connecting to different slices on the 5G systems. The slice performance for one slice should not be affected by other slice traffic. This paper evaluates the performance of E2E 5G slicing in terms of throughput, jitter, reliability, transmission rate and mobility under different circumstances. In the proposed system, the performance of the slice is checked when the user connects to eight slices or more at the same time. In addition, we propose a slice termination and connection algorithm that allows the user to register new slices. Moreover, the algorithm allows users who are already registered to be released after using slices, enabling more effective use of the network resources.

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“…The C-RAN architecture aims at warranting quality of service (QoS) to all users based on the following three components: (a) the remote radio head (RRH), which includes the antenna together with the radio frequency components and has the duty for the signal's transmission/reception, (b) the baseband units (BBUs), which form a BBU pool and are responsible for the baseband signal processing, and (c) a high-capacity fronthaul network, which connects the BBU pool with the RRHs via the common public radio interface (CPRI) [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

et al. 2022

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In this paper, a cloud radio access network (C-RAN) is considered where the baseband units form a pool of computational resource units (RUs) and are separated from the remote radio heads (RRHs). The RRHs are grouped into clusters based on their capacity in radio RUs. Each RRH serves different service-classes whose calls have different requirements in terms of radio and computational RUs and follow a compound Poisson process. This means that calls arrive in batches while each batch of calls follows a Poisson process. If the RUs’ requirements of an arriving call are met, then the call is accepted in the serving RRH for an exponentially distributed service time. Otherwise, call blocking occurs. We initially start our analysis with a single-cluster C-RAN and model it as a multiservice loss system, prove that the model has a product form solution, and determine time and call congestion probabilities via a convolution algorithm. Furthermore, the previous model is extended to include the more complex case of many clusters of RRHs.

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A Survey of Mobility Management as a Service in Real-Time Inter/Intra Slice Control

Cited by 12 publications

References 95 publications

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

Multiservice Loss Models in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

Performance Evaluation for End-to-End Slice Management in 5G/B5G Cellular Networks

Multiservice Loss Models in C-RAN Supporting Compound Poisson Traffic

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