Modelling the time-varying cell capacity in LTE networksBart Sas · Elena Bernal-Mor · Kathleen Spaey · Vicent Pla · Chris Blondia · Jorge Martinez-Bauset adaptive modulation and coding (AMC) is applied. With AMC, different modulation and coding schemes (MCSs) are used to serve different users in order to maximise the throughput and range. The used MCS depends on the quality of the radio link between the base station and the user. Data is sent towards users with a good radio link with a high MCS in order to utilise the radio resources more efficiently while a low MCS is used for users with a bad radio link. Using AMC however has an impact on the cell capacity as the quality of a radio link varies when users move around; this can even lead to situations where the cell capacity drops to a point where there are too little radio resources to serve all users. AMC and the resulting varying cell capacity notably has an influence on admission control (AC). AC is the algorithm that decides whether new sessions are allowed to a cell or not and bases its decisions on, amongst others, the cell capacity. The analytical model that is developed in this paper models a cell with varying capacity caused by user mobility using a continuous-time Markov chain (CTMC). The cell is divided into multiple zones, each corresponding to the area in which data is sent towards users using a certain MCS and transitions of users between these zones are considered. The accuracy of the analytical model is verified by comparing the results obtained with it to results obtained from simulations that model the user mobility more realistically. This comparison shows that the analytical model captures the varying cell capacity very accurately; only under extreme conditions differences between the results are noticed.The developed analytical and simulation models are then used to investigate the effects of a varying cell capacity on AC. The analytical and simulation models are also used to study an optimisation algorithm that adapts the parameter of the AC algorithm which deThe final publication is available at Springer via http://dx.doi.org/10.1007/s11235-013-9782-2 2 Bart Sas et al.termines the amount of resources that are reserved in order to mitigate the effects of the varying cell capacity. Updating the parameter of the AC algorithm is done by reacting to certain triggers that indicate good or bad performance and adapt the parameters of the AC algorithm accordingly. Results show that using this optimisation algorithm improves the quality of service (QoS) that is experienced by the users.

Abstract-We model a cognitive radio system as a quasibirth-death (QBD) process and determine its performance parameters. We also model the system at the quasi-stationary limiting regime. We show that this regime defines the asymptotic system behavior. The performance parameters of interest at this regime are independent of the service time distributions and can be determined by simple recursions. We propose and evaluate a new methodology to determine when the quasi-stationary approximation can be considered a good approximation of the actual system behavior. It requires low computational cost and does not require to solve the exact system.

In this paper we compute the optimal configuration of the Multiple Fractional Guard Channel (MFGC) admission policy in multiservice mobile wireless networks. The optimal configuration maximizes the offered traffic that the system can handle while meeting certain QoS requirements but computing the optimal parameter setting of this policy can constitute a high computational cost. To face these computational limitations an approximation based on Kaufman & Roberts recursion is evaluated and an algorithm is proposed. Moreover, we also propose an adaptative algorithm. The numerical results show that it is not easy to find a fast and accurate algorithm, in this sense the adaptative method yields the best results.

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Bernal Mor, E.; Pla, V.; Martínez Bauset, J.; Luis Guijarro (2016) Abstract-During the last years, mobile cellular networks have witnessed an enormous growth in the carried data-traffic volume. The current networks' features are not enough to cope with this traffic trend and the concept of small cells has emerged as a feasible solution to increase the network capacity. However, the deployment of small cells introduces several technical challenges such as the cross-tier interference between the macrocell and the small cells, or the use of the subscriber land-line to send the backhaul data. In this paper, an analytical model is proposed to study the impact that the user traffic dynamics, the mobility of macrocell users, the scheme chosen to associate macrocell users to the small cells and the changing available capacity of the small cells backhaul have on the system performance. To make the solution of the model computationally feasible, we exploit the time-scale decomposition approach. In most practical scenarios, the arrival and departure rates of traffic flows are much larger than the rate of events associated with the mobility of macrocell users. Then, flows perceive that macrocell users are still. This model is applied to identify the scheme to associate macrocell users to the small cells which maximizes the performance perceived by the small cell users.

Contemporary wireless networks like Long Term Evolution (LTE) employ a technique called adaptive modulation and coding (AMC) to enhance the throughput of the users in the system. Applying AMC however causes the total cell capacity to vary over time as sessions are started and stopped and users move around. The varying cell capacity has an impact on the quality of service (QoS) experienced by the users and also on the admission control (AC) algorithms used for such system as the variation of the cell capacity can cause the cell capacity to drop below the required amount that is needed to service all users in a cell.In this paper we present an analytical model that models this time-varying cell capacity and compare the results obtained with it to results obtained from more realistic simulations in order to verify the modelling assumptions made in the analytical model. We then use both the analytical model and the simulations to study the impact of the time-varying cell capacity on a simple AC scheme. Scenarios in which various parameters are varied are simulated and the results of both models are compared to each other.The results obtained from the analytical model and the simulations show that the analytical model is very accurate. The differences between the results only differ up to a couple of tenths of a percent. Only in extreme conditions both models differ. We also identify the cases and the reason why both models differ.

The constant evolution of mobile-phone traffic demands for novel networking solutions especially focused on indoor environment. In this context, the use of femtocells, i.e., cells with very limited coverage area, has been proposed. In this paper, a femtocell network with hybrid access control mode is considered. The activity profile of the Femtocell Users (FUs) is modeled to compute the maximum achievable throughput and the consumed energy per successfully transmitted data bit by the Macrocell Users (MUs), depending on which set of channels are operated in open access mode, i.e., which channels can be used by MUs. Thus, it is identified how many and which channels must be operated in open access mode, depending on the physical capacities of the channels and the amount of time these channels are not occupied by FUs. The results motivate the need for novel resource management schemes which can dynamically adapt the set of open access channels to the network conditions.