Modelling time-dependent aggregate traffic in 5G networks

Vijayalakshmi Chetlapalli (), K. S. S. Iyer () and Himanshu Agrawal ()
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Vijayalakshmi Chetlapalli: Symbiosis Institute of Technology (Constituent of Symbiosis International University)
K. S. S. Iyer: Member, IEEE
Himanshu Agrawal: Symbiosis Institute of Technology [Constitutent of Symbiosis International (Deemed University)]

Telecommunication Systems: Modelling, Analysis, Design and Management, 2020, vol. 73, issue 4, No 6, 557-575

Abstract: Abstract Future wireless networks like 5G will carry an increasingly wide variety of data traffic, with different QoS requirements. In addition to conventional data traffic generated from HTTP, FTP and video streaming applications by mobile broadband users [human-type communication (HTC)], traffic from machine-to-machine (M2M) and Internet-of-Things (IoT) applications [machine-type communication (MTC)] has to be supported by 5G networks. Time-of-day variation in arrival rate of connection-level requests and randomness in length of data sessions in HTC result in randomness in aggregate traffic. In MTC, randomness in traffic arises from random number of devices trying to connect to the base station at any given time. Traffic generated by MTC devices may be either periodic or event-triggered. Nevertheless, it is difficult to model aggregate traffic due to non-stationary nature of traffic generated by each type of service. In this paper, special correlation functions of stochastic point processes called Product Densities (PDs) are used for estimating aggregate traffic under non-stationary arrival rates. For HTC, PDs are defined for estimating time-dependent offered load of connection-level service requests and expected number of ON periods in an interval of time $$(0,T)$$(0,T). The aggregate traffic is evaluated for light-tail (exponential) and heavy-tail (hyper exponential) servicing times. For MTC, PDs are defined for estimating the random number of devices connected to the base station at any time. Another QoS parameter of interest in high speed networks is the expected number of service requests/devices delayed beyond a critical value of delay. Bi-variate PD is defined to estimate the number of service requests/devices delayed beyond a given critical threshold. The results from PD model show close agreement with simulation results. The proposed PD technique proves effective in performance analysis under time-dependent traffic conditions, and is versatile for application to several studies in wireless networks including power consumption, interference and handover performance.

Keywords: Time-dependent aggregate traffic; Human type communication; Machine type communication; Internet-of-Things; Stochastic point processes; Product densities; Delay; ON–OFF traffic; 68M20; 90B22; 60G55 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11235-019-00629-w

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