Modelling the time-varying cell capacity in LTE networks

Abstract

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.

Design and evaluation of admission control policies in mobile cellular networks

“…For R (1) , it is better to operate in OA mode the channels with the lowest data rate. However, for R (2) , the channels with the highest data rate yield better performance. This can be explained as follows.…”

“…This happens because FUs use first channels with the highest data rates and hence, these channels experience more interruptions. (1) and R (2) .…”

“…8.6, results considering different sets of data rates for each channel are shown. We consider R (1) as the set of data rates defined in Section 8.4.1 and R (2) are the set of data rates with values from Table 8.1 corresponding to rows #1, 3, 5, 7, 10, 12, 14 and 16 . We have R (1) …”

“…When the set of channels operated in OA mode have the highest data rate, the performance only has better results when the difference of data rates among channels is very significant (R (2) ). Since common scenarios does not present these asymmetrical data rates, from now on R (1) is considered, and the set of channels operated in OA mode are considered to be the channels with the lowest data rate.…”

“…For clarity, the notation has been simplified as a s (x) = a s . The transition due to an aborted SU is represented for the diagonal transitions from state (2, 3) to (3,2) and from state (3, 2) to (4, 1).…”