Performance Analysis of M2M Traffic in LTE Network Using Queuing Systems with Random Resource Requirements

Sopin, Eduard; Ageev, Kirill A.; Markova, Ekaterina; Vikhrova, Olga; Gaidamaka, Yuliya

doi:10.3103/s0146411618050127

Cited by 19 publications

(12 citation statements)

References 8 publications

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“…For the generation of random numbers with CDF   The simulation results were compared with calculations from [7], where performance measures of the queueing system were obtained using sampling of the CDF (7) and applying recurrence algorithm for discrete resource requirements (figures 3 and 4). As it is seen from figure 4, the blocking probability growth slows down with the increase of the load.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…However, analytical formulas for probabilistic characteristics are too complex to be used directly due to multiple convolutions of the resource requirements CDF. In [7], we derived the recurrent algorithm for evaluation of stationary measures for the case of discrete resource requirements and proposed the sampling approach for continuous resources. However, the complexity of the calculations is still high and the algorithms are only applicable under assumption of Poisson arrivals.…”

Section: Introductionmentioning

confidence: 99%

“…In the paper, we describe the developed simulation tool and use it to evaluate performance measures of M2M traffic in a LTE network cell. By the way, we provide comparison of calculations accuracy with the methods, proposed in [7]. The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simulation Of The Limited Resources Queuing System For Performance Analysis Of Wireless Networks

Sopin¹,

Ageev²,

Shorgin³

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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Queuing systems with limited resources, in which customers require a device and a certain amount of limited resources for the duration of their service, proved their effectiveness in the performance analysis of modern wireless networks. However, the application of the queuing systems leads to complex computations. In this paper, we develop the simulation tool for the limited resources queuing systems and apply it to the analysis of M2M traffic characteristics in a LTE network cell.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simulation Of The Limited Resources Queuing System For Performance Analysis Of Wireless Networks

Sopin¹,

Ageev²,

Shorgin³

2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

View full text Add to dashboard Cite

show abstract

“…The main reason is the need for evaluating multiple convolutions of the resource requirements distribution as discussed above. However, the authors in [161], developed a recursive algorithm for calculating the normalization constant constant Q 0 , which allows for efficient numerical analysis.…”

Section: B Baseline Resource Queuing Systemsmentioning

confidence: 99%

A Tutorial on Mathematical Modeling of Millimeter Wave and Terahertz Cellular Systems

Moltchanov¹,

Sopin²,

Begishev³

et al. 2021

Preprint

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Millimeter wave (mmWave) and terahertz (THz) radio access technologies (RAT) are expected to become a critical part of the future cellular ecosystem providing an abundant amount of bandwidth in areas with high traffic demands. However, extremely directional antenna radiation patterns that need to be utilized at both transmit and receive sides of a link to overcome severe path losses, dynamic blockage of propagation paths by large static and small dynamic objects, macro-and micromobility of user equipment (UE) makes provisioning of reliable service over THz/mmWave RATs an extremely complex task. This challenge is further complicated by the type of applications envisioned for these systems inherently requiring guaranteed bitrates at the air interface. This tutorial aims to introduce a versatile mathematical methodology for assessing performance reliability improvement algorithms for mmWave and THz systems. Our methodology accounts for both radio interface specifics as well as service process of sessions at mmWave/THz base stations (BS) and is capable of evaluating the performance of systems with multiconnectivity operation, resource reservation mechanisms, priorities between multiple traffic types having different service requirements. The framework is logically separated into two parts: (i) parameterization part that abstracts the specifics of deployment and radio mechanisms, and (ii) queuing part, accounting for details of the service process at mmWave/THz BSs. The modular decoupled structure of the framework allows for further extensions to advanced service mechanisms in prospective mmWave/THz cellular deployments while keeping the complexity manageable and thus making it attractive for system analysts.

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“…The proposed work is the first that studies a C-RAN that accommodates multiservice quasi-random traffic and, at the same time, provides convolution algorithms for the efficient determination of congestion probabilities (recently, the case of C-RAN multi-service random traffic has been proposed in [46]). Such algorithms are used in the literature in order to express complicated resource sharing policies such as the bandwidth reservation policy and threshold-based policies [47][48][49][50][51][52][53][54]. The proposed models of this paper are named finite multi-class-single-cluster (f-MC-SC) and finite multiclass-multi-cluster (f-MC-MC), respectively, while the summary of our contribution is as follows: (1) we propose the f-MC-SC model and show that the model has a PFS, (2) we present a brute force (BF) analytical method together with a convolution algorithm for the calculation of congestion probabilities in the proposed model, (3) we compare the congestion probabilities results of the f-MC-SC model with simulation results and those obtained via [27], (4) we propose the f-MC-MC model and show that the model has a PFS, and (5) we present a BF method as well as a convolution algorithm for the determination of congestion probabilities in the f-MC-MC model.…”

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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Performance Analysis of M2M Traffic in LTE Network Using Queuing Systems with Random Resource Requirements

Cited by 19 publications

References 8 publications

Simulation Of The Limited Resources Queuing System For Performance Analysis Of Wireless Networks

Simulation Of The Limited Resources Queuing System For Performance Analysis Of Wireless Networks

A Tutorial on Mathematical Modeling of Millimeter Wave and Terahertz Cellular Systems

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

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