This article introduces an enhanced version of previously developed self-optimizing algorithm that controls the handover (HO) parameters of a long-term evolution base station in order to diminish and prevent the negative effects that can be introduced by HO (radio link failures, HO failures and ping-pong HOs) and thus improve the overall network performance. The default algorithm selects the best hysteresis and time-to-trigger combination based on the current network status. The enhancement proposed here aims to maximize the gain provided by the algorithm by improving its convergence time. The effects of this enhancement have been studied in a rural scenario setting and compared to the original algorithm; the results show a clear improvement, faster convergence, and better network performance, because of the enhancement.

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.

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In mobile cellular networks the handover (HO) algorithm is responsible for determining when calls of users that are moving from one cell to another are handed over from the former to the latter. The admission control (AC) algorithm, which is the algorithm that decides whether new (fresh or HO) calls that enter a cell are allowed to the cell or not, often tries to facilitate HO by prioritising HO calls in favour of fresh calls. In this way, a good quality of service (QoS) for calls that are already admitted to the network is pursued. In this paper, the effect of self-optimisation of AC parameters on the HO performance in a long term evolution (LTE) network is studied, both with and without the self-optimisation of HO parameters. Simulation results show that the AC parameter optimisation algorithm considerably improves the HO performance by reducing the amount of calls that are dropped prior to or during HO.

Mobile Network Operators (MNOs) are integrating carrier-grade Wireless Local Area Network (WLAN) to cellular networks to improve network performance and user experience. Access network selection (ANS) between cellular and WLAN plays a key role in the integration. Given the complexity of heterogeneous networks characterized by multiple network layer deployments and inhomogeneous traffic distribution, the ANS has to automatically adapt to dynamic network conditions. In this article, we present and evaluate a Self-Organizing Network (SON) algorithm for tuning the ANS between the Long Term Evolution (LTE) and WLAN systems. The proposed SON algorithm adopts a WLAN received signal strength (RSS) threshold to control the access selection. The RSS threshold is updated by the SON algorithm based on periodically monitored load in the LTE and WLAN systems. The SON algorithm is evaluated by simulations in realistic heterogeneous network scenarios and proved effective in improving user experience.

In currently deployed cellular networks, a lot of time and effort is put by operators into the optimization of radio resource management functions. The use of selfoptimization algorithms will eliminate the need for manual monitoring and offline analysis, as these algorithms constantly strive to improve network performance in an automated fashion. However, expectations have to be kept realistic as in some cases it may not be possible for network performance to be significantly improved. A trade-off has to be made between the performance enhancement that these algorithms can achieve and the resources they need for doing so. This article proposes a mechanism which will minimize signalling determined by the use of a handover (HO) self-optimization algorithm. This algorithm derives the proper values of the main HO control parameters based on the observed network performance, while the signalling minimizing mechanism will be used as a stop condition, as, due to shadow fading or coverage holes, performance can only be improved up to a point. After this point, performance will only slightly vary with different control parameter settings but the observed gain will not compensate for the possible instability and signalling load introduced by these changes. By using the signalling minimizing mechanism in combination with the HO self-optimization algorithm, network performance will be maintained while the signalling load will be significantly diminished

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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.