Exploiting Machine Learning for the Performance Analysis of a Mobile Hotspot with a Call Admission Control Mechanism

Keramidi, Irene; Uzunidis, Dimitris; Vlasakis, Marinos; Sarigiannidis, Panagiotis; Moscholios, Ioannis D.

doi:10.1109/camad55695.2022.9966911

Cited by 1 publication

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“…A possible springboard for such an application can be considered in the work of Ref. [30] where the accuracy and estimation time of 6 ML algorithms has been studied for the non-priority scheme proposed in Ref. [11].…”

Section: Related Workmentioning

confidence: 99%

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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Section: Related Workmentioning

confidence: 99%