Skip to main content

Integration of a genetic optimisation algorithm in a simulation framework for optimising femtocell networks.

Qureshi, H., 2021. Integration of a genetic optimisation algorithm in a simulation framework for optimising femtocell networks. Doctoral Thesis (Doctoral). Bournemouth University.

Full text available as:

QURESHI, Haseeb Ahmad_Ph.D._2021.pdf
Available under License Creative Commons Attribution Non-commercial.



The developments in mobile communication systems from 1G to 4G have increased demands on the network due to the increased number of devices and increasing volume of data and 5G is expected to significantly increase demands further. Therefore, networks need to be more efficient to deliver the expected increase in volume. An energy and cost efficient way to cope with such an anticipated increase in the demand of voice and data is the dense deployment of small cells i.e. femtocells. Femtocells are identified as a crucial way to the delivery of the increased demands for heterogeneous networks in which macrocells work in combination with femtocells to provide coverage to offices, homes and enterprise. A survey of the literature is conducted to examine the mechanisms and approaches different authors have used to optimise the network. One of the major activities in this project before the transfer was the identification of the parameters. The literature was analysed and key performance parameters were identified. Based on the identified key performance parameters, a simulation framework is used to perform the experiments and to analyse the performance of a two-tier LTE-A system having femtocell overlays. A comprehensive and easy to use graphical user interface has been set up with the desired two- tier network topologies. It estimates the throughput and path loss of all the femto and macro users for all the supported bandwidths of an LTE-A system using different modulation schemes. A series of tests are carried out using the described simulation framework for a range of scenarios. The modulation scheme that yield highest throughput for a femtocell user is identified, and path loss is found to be independent from the modulation scheme but is dependent on the distance from its base station. In another series of experiments, the effects that walls inside buildings have on connectivity are examined and positioning of the femtocells is changed for each scenario inside buildings to analyse the performance. These results are used to find the optimised location of femtocells in different room layouts of the building. The simulation framework is further developed to be able to optimise the whole femtocell network by finding the optimised positioning of femtocells using the genetic optimisation algorithm. The end user can provide the inputs of the desired network topology to the simulation framework through a graphical user interface. The throughput and path loss of all the femto users are calculated before and after optimisation. The simulation results are generated in the form of tables before and after optimisation for comparison and analysis. The layouts depicting the indoor environment of the building before and after optimisation can be seen and analysed through the graphical user interface developed as a part of this simulation framework. Two case studies are defined and described to test the capacity and capability of the developed simulation framework and to show how the simulation framework can be used to identify the optimum positions of the femtocells under different configurations of room designs and number of users that represent contrasting loads on the network. Any desired network topology can be created and analysed on the basis of throughput and path loss by using this simulation framework to optimise the femtocell networks in an indoor environment of the building. The results of the experiments are compared against the claims in other published research.

Item Type:Thesis (Doctoral)
Additional Information:If you feel that this work infringes your copyright please contact the BURO Manager.
Uncontrolled Keywords:genetic algorithm; optimisation; femtocell networks
Group:Faculty of Science & Technology
ID Code:36003
Deposited By: Symplectic RT2
Deposited On:14 Sep 2021 15:16
Last Modified:14 Mar 2022 14:29


Downloads per month over past year

More statistics for this item...
Repository Staff Only -