Performance Evaluation of a C-RAN Supporting Quasi-Random Traffic

Chousainov, Iskanter-Alexandros; Moscholios, Ioannis D.; Kaloxylos, Alexandros; Logothetis, Michael D.

doi:10.23919/softcom.2019.8903712

Cited by 9 publications

(28 citation statements)

References 13 publications

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“…The K-R formula is recursive and therefore leads to an efficient way for the CBP calculation in a loss system that services multirate Poisson traffic. Because of this, the interested reader can find many applications of the K-R formula in PFS and non-PFS models [46][47][48][49][50][51][52][53][54][55][56][57][58].…”

Section: The P-s Multirate Loss Model-a Reviewmentioning

confidence: 99%

An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic

et al. 2019

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We consider the downlink of an orthogonal frequency division multiplexing (OFDM)-based cell that services calls from many service-classes. The call arrival process is random (Poisson) or quasi-random, i.e., calls are generated by an infinite or a finite number of sources, respectively. In order to determine congestion probabilities and resource utilization, we model the cell as a multirate loss model. Regarding the call admission, we consider the restricted accessibility, the bandwidth reservation (BR), and the complete sharing (CS) policies. In a system of restricted accessibility, a new call may be blocked even if resources do exist. In a BR system, subcarriers can be reserved in favor of calls of high subcarrier requirements. Finally, in a CS system, a new call is blocked due to resource unavailability. In all three policies, we show that there exist recursive formulas for the determination of the various performance measures. Based on simulation, the accuracy of the proposed formulas is found to be quite satisfactory.

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Section: The P-s Multirate Loss Model-a Reviewmentioning

confidence: 99%

An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic

et al. 2019

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“…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. On the other hand, there are very few papers that consider CAC in the C-RAN and provide analytical formulas or algorithms for the CBP determination ( [33]- [35], [37]- [39]).…”

Section: Introductionmentioning

confidence: 99%

“…In that case, the RRHs that belong to a cluster have the same amount of radio RUs. Recently, the authors have extended the SC-SC model to include the case of a quasi-random call arrival process [39]. This process appears when the RRHs serve calls generated by a finite number of MUs.…”

Section: Introductionmentioning

confidence: 99%

“…In that sense, the quasirandom process is smoother than the Poisson process (where calls are generated by an infinite number of users) [7]. We name the model of [39], finite SC-SC (f-SC-SC) model.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we focus on the models of [38] and [39]. More precisely, we extend the SC-MC model of [38] by incorporating the quasi-random call arrival process.…”

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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Performance evaluation of a mobile hotspot supporting quasi‐random traffic

et al. 2023

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The authors study and evaluate a mobility‐aware call admission control algorithm in a mobile hotspot. More specifically, a vehicle which has an access point of a fixed capacity and may alternate between stop and moving phases is considered. In the stop phase, the vehicle services new and handover calls. To prioritise handover calls a probabilistic bandwidth reservation policy is considered where a fraction of the capacity is reserved for handover calls. Based on this policy, new calls may enter the reservation space with a predefined probability. In addition, handover calls have the option to wait in a queue of finite size if there are no available resources at the time of their arrival. In the moving phase, the vehicle services only new calls under the classical complete sharing policy. In both phases, calls arrive in the system according to a quasi‐random process, require a single bandwidth unit for their acceptance in the system and have an exponentially distributed service time. To analytically determine the various performance measures, such as time congestion probabilities, call blocking probabilities and link utilisation, an accurate analytical method is presented based on three‐dimensional Markov chains.

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Performance Evaluation of a C-RAN Supporting Quasi-Random Traffic

Cited by 9 publications

References 13 publications

An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic

An Analytical Framework in OFDM Wireless Networks Servicing Random or Quasi-Random Traffic

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

Performance evaluation of a mobile hotspot supporting quasi‐random traffic

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