Analytical Modelling of a New Handover Algorithm to Improve Allocation of Resources in Highly Mobile Environments

Abstract

Wireless and mobile communication systems have evolved considerably in recent years. Seamless mobility is one of the main challenges facing mobile users in wireless and mobile systems. However, highly mobile users lead to a high number of handover failures and unnecessary handovers due to the limited resources and coverage limitations with a high mobile speed. The traditional handover models are unable to cope with high mobile users in such environments. This paper proposes, an intelligent handover decision approach to minimize the probability of handover failures and unnecessary handovers whilst maximizing the usage of resources in highly mobile environments. The proposed approach is based on modelling the system using a Markov chain to enhance the system's performance in terms of blocking probability, mean queue length and transmission delay. The results are compared with the traditional handover model. Simulation is also employed to validate the accuracy of the proposed model. Numerical results have shown that the proposed method outperforms the traditional algorithm over a wide range of handover failures and significantly reduced the number of such failures and unnecessary handovers. The results of this study show that quality if service (QoS) measures of such systems can be evaluated efficiently and accurately using the proposed analytical model. However, the performance results have also shown that it is still necessary to explore an effective model for operational spaces. In addition, the proposed model can also be adapted to various types of networks considering the high speed of the mobile user and the radius of the network.

Determination of the Traffic Properties of Cells with Mobile Users Using a Mixed Traffic

“…Nevertheless, in the conditions of greater users' moving speed it is necessary to carefully anticipate the moment of new cell selection when handover is realized [12]. Such an analysis decreases the number of unnecessary handovers due to the lack of idle channels or idle waiting spaces, but also it is necessary to implement new, specific traffic models [13]. As, generally, handover traffic is more smoothed than Poisson process, the practically expected results in the case of the significant traffic load and smaller cells are better than the results presented by Poisson model.…”

Exploring Analytical Models for Performability Evaluation of Virtualized Servers using Dynamic Resource

“…In addition, many existing studies mainly focus on performance analysis of virtual servers in cloud computing systems [5,6,10,11,17,20]. Performance modelling and evaluation of the virtual servers in the cloud computing is considered in [5].…”

“…As shown in Fig. 1, the tasks join the system with the Poisson distribution, hence the average arrival rate is λ [4,10,11]. On the other hand, the physical and virtual machines in the system are prone to failures, each VMs and PM get corrupted with an average rate of ξ v and ξ p , respectively.…”

Enhanced handover algorithm in LTE femtocells network

“…The handover threshold and the exit threshold can be imagined as two different hexagons with an irregular radius around the cell core. The inner hexagon with small radius refers to the exit threshold hexagon, and the outer hexagon with larger radius refers to the handover threshold hexagon [19] .…”

“…follows a" spiral" path such that the numbers of hexagons per ring are multiple of 6, e.g. the first ring (1-6), the second ring (7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18), the third ring (19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36), and the last ring (37-60).…”