On the Fronthaul Statistical Multiplexing Gain

Wang, Liumeng; Zhou, Sheng

doi:10.1109/lcomm.2017.2653120

Cited by 20 publications

(12 citation statements)

References 10 publications

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“…Q fin,(−(z,1)) (r)+q fin (T ) (25) where: q fin,z,1 (C z ) refers to the case of unavailable radio RUs in the (z, 1) RRH (already determined in step 1), Q fin,(−(z,1)) (r) are the normalized values of Q fin,(−(z,1)) (r), while q fin (T ) refers to the un-normalized probability of unavailable computational RUs, given by q…”

Section: B the Proposed Convolution Algorithmmentioning

confidence: 99%

“…On the one hand, various aspects of the C-RAN architecture have been investigated and analyzed the last few years, such as the capacity demands and possible functional splits on the fronthaul network [25], [26], energy and cost saving issues [27], [28], security challenges [29], resource allocation issues related to RRH selection, spectrum management and throughput maximization [30], as well as the dimensioning problem of the necessary number of V-BBU required to handle a specific number of RRHs [31], [32]. The latter focus only on the V-BBU and model them as a queueing system in which the arrival process of jobs follows a batched Poisson process and the service time is exponentially distributed.…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

Chousainov

Moscholios

Kaloxylos

et al. 2020

J. commun. softw. syst. (Online)

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In this paper, a cloud radio access network (C-RAN) is considered where the remote radio heads (RRHs) are separated from the baseband units (BBUs). The RRHs in the C-RAN are grouped in different clusters according to their capacity while the BBUs form a centralized pool of computational resource units. Each RRH services a finite number of mobile users, i.e., the call arrival process is the quasi-random process. A new call of a single service-class requires a radio and a computational resource unit in order to be accepted in the C-RAN for a generally distributed service time. If these resource units are unavailable, then the call is blocked and lost. To analyze the multi-cluster C-RAN, we model it as a single-rate loss system, show that a product form solution exists for the steady state probabilities and propose a convolution algorithm for the accurate determination of congestion probabilities. The accuracy of this algorithm is verified via simulation. The proposed model generalizes our recent model where the RRHs in the C-RAN are grouped in a single cluster and each RRH accommodates quasi-random traffic.

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Section: B the Proposed Convolution Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

Chousainov

Moscholios

Kaloxylos

et al. 2020

J. commun. softw. syst. (Online)

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show abstract

“…In this work, we consider that the UEs are connected to the nearest RRU. Therefore, the cell coverage area per RRU can be estimated analytically using Voronoi diagrams as performed in [12]. However, unlike [12], where authors assume arbitrary transmission rates depending on user's behavior, and make use of a functional split architecture, we assume a fully centralized scheme with standard CPRI rates [5], listed in the Table I.…”

Section: System Modelmentioning

confidence: 99%

A Variable Rate Fronthaul Scheme for Cloud Radio Access Networks

Das

Ruffini

2019

J. Lightwave Technol.

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Cloud Radio Access Networks (C-RANs) are considered one of the most promising candidates for implementing 5G mobile communication systems. C-RAN enables centralisation of baseband processing, enabling advanced coordination between base stations, such as coordinated multi-point and inter-cell interference cancellation. In addition, it allows pooling of resources across several cells, providing statistical multiplexing gains of computing resources. However, the link between the remote radio unit and the baseband unit requires high transmission capacity, making the fronthaul link a potential bottleneck for future dense cell deployments. One of the current solutions to this issue is to compress the fronthaul transmission rate. A second under standardisation is to adopt a different functional split that can reduce the transmission capacity requirement. While this solution decreases the capacity requirements on the transport link, it decentralises some of the computational resources, requiring a more complex remote radio unit (e.g., compared with a CPRI type of solution), whose resources cannot be utilised by other cells when not in use. It is thus expected that in the future, multiple solutions (different functional splits and CPRI) will coexist.In this paper, we introduce the concept of Variable Rate Fronthaul (VRF) for C-RAN. This scheme operates on a CPRI type of interface (e.g., one that transmits I/Q data samples) with the novelty of dynamically changing the cell bandwidth, and consequently the fronthaul data rates, depending on the cell load, with the support of a Software Defined Network (SDN) controller. This allows for a more efficient transport of C-RAN cells' data over a shared backhaul. We first propose a mathematical analysis of the VRF performance using a queuing theory approach based on the Markov model. We then provide the results of our simulation framework both for validation and in support of the mathematical analysis. Our results show that the proposed VRF scheme provides significantly lower blocking probability over a shared backhaul than standard CPRI.

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“…Ref. [27] proposes a model to analyze the fronthaul statistical multiplexing gain brought by the spatial randomness of the traffic when aggregating several remote radio units as a cluster to share a fronthaul link.…”

Section: Related Workmentioning

confidence: 99%

Multiplexing Gain and Processing Savings of 5G Radio-Access-Network Functional Splits

Shehata

Elbanna

Musumeci

et al. 2018

IEEE Trans. on Green Commun. Netw.

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Centralized Radio Access Network (C-RAN) is a promising mobile network architecture designed to support the requirements of future 5G mobile networks. In C-RAN, the "centralization" of baseband units enables substantial savings of computational resources (what we call "multiplexing gain" in this paper) and significant power savings. On the other hand, the deployment of C-RAN requires high capacity and imposes strict latency requirements on the fronthaul transport-network. To address these issues, various alternative architectures, known as "RAN functional splits", have been introduced to relax these strict fronthaul requirements. In this paper, we perform a quantitative analysis of the computational savings and the resulting power savings enabled by C-RAN, considering different RAN functional splits. To this end, we analytically model RAN computational resources to evaluate the multiplexing gain for different RAN functional splits. This model allows to calculate the processing reduction occurring in each RAN functional split. We then use this model to estimate the power savings of the various functional splits, considering different assumptions in terms of geographical areas, users distribution and number of aggregated cell sites. We find that up to 28% computational resources savings and 24% power savings can be achieved through functional splits in comparison to Distributed RAN.Index Terms-C-RAN, multiplexing gain, computational effort, functional splits, GOPS.

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On the Fronthaul Statistical Multiplexing Gain

Cited by 20 publications

References 10 publications

Performance Evaluation in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

Performance Evaluation in Single or Multi-Cluster C-RAN Supporting Quasi-Random Traffic

A Variable Rate Fronthaul Scheme for Cloud Radio Access Networks

Multiplexing Gain and Processing Savings of 5G Radio-Access-Network Functional Splits

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