Congestion probabilities in the X2 link of LTE networks

Panagoulias, Panagiotis I.; Moscholios, Ioannis D.

doi:10.1007/s11235-018-00537-5

Cited by 14 publications

(10 citation statements)

References 51 publications

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“…Equation ( 11) is the second equation to be used for the calculation of P(0,0) and P(1,0). Having determined the values of P(0,0) and P(1,0) (via ( 9) and (11), respectively) and the values of S 0 i,n and S 1 i,n (via ( 6) and (7), respectively), we can calculate the values of P(i,n) via (4) and, consequently the CBP of new and handovers calls together with the link utilization, according to the next subsection.…”

Section: An Iterative Algorithm For the Determination Of P(in)mentioning

confidence: 99%

“…The term CBP refers to the blocking of new or handover calls due to the unavailability of b.u. To reduce CBP (especially of handover calls), various bandwidth sharing policies exist in the literature, with the most common being the guard channel or bandwidth reservation (BR) policy [5][6][7][8][9][10][11][12]. The main characteristic of the BR policy is that it introduces a priority to handover calls by reserving b.u.…”

Section: Introductionmentioning

confidence: 99%

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Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

Keramidi

Moscholios

Sarigiannidis

2021

Telecom

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In this paper we study a mobility-aware call admission control algorithm in a mobile hotspot. To this end, a vehicle is considered which has an access point with a fixed capacity. The vehicle alternates between stop and moving phases. When the vehicle is in the stop phase, it services new and handover calls by prioritizing them via a probabilistic bandwidth reservation (BR) policy. Based on this policy, new handover calls may enter the reservation space with a predefined probability. When the vehicle is in the moving phase, it services new calls only. In that phase, two different policies are considered: (a) the classical complete sharing (CS) policy, where new calls are accepted in the system whenever there exists available bandwidth, and (b) the probabilistic BR policy. Depending on the selected policy in the moving phase, we propose the probabilistic BR loss model (if the CS policy is selected) and the generalized probabilistic BR loss model (if the probabilistic BR policy is selected). In both stop and moving phases, where the call arrival process is Poisson, calls require a single bandwidth unit in order to be accepted in the system, while the service time is exponentially distributed. To analytically determine call blocking probabilities and the system’s utilization, we propose efficient iterative algorithms based on two-dimensional Markov chains. The accuracy of the proposed algorithms is verified via simulation.

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Section: An Iterative Algorithm For the Determination Of P(in)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

Keramidi

Moscholios

Sarigiannidis

2021

Telecom

Self Cite

View full text Add to dashboard Cite

show abstract

“…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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“…However, in modern networks it is significant to be able to study a multiservice environment where calls need more resources. The analysis at call-level of such contemporary networks is essential in network planning procedures [31][32][33][34][35][36][37][38][39][40][41][42][43].…”

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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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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Congestion probabilities in the X2 link of LTE networks

Cited by 14 publications

References 51 publications

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

Call Blocking Probabilities under a Probabilistic Bandwidth Reservation Policy in Mobile Hotspots

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

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

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