Degree Name

Doctor of Philosophy


School of Electrical, Computer and Telecommunications Engineering - Faculty of Informatics


This thesis examines the problem of admission control in multi-service mobile communications networks. The work is divided into two parts, chapters 2, 4 and 5 look at multi-service admission control in the mobile domain, while Chapters 3, 6 and 7 propose Fuzzy and Neuro-Fuzzy Admission Control schemes for Multi-service mobile networks. The thesis begins by examining guard channel techniques for guaranteeing better performance for handover connections with different classes of traffic. An approximate analysis for fixed multi-service networks is extended into the mobile domain. This analysis is then verified through simulation. The analysis of the mobile network is further extended to include the micro-cellular case. The simulation results and analysis lead to the development of a heuristic for the amount of bandwidth that needs to be reserved for different multi-service scenarios. A fundamental contribution of this thesis is the development of admission control schemes that apply to networks with a general cell dwell time distribution (such as lognormal and gamma distributions) that are different to the classic guard channel problem. In this thesis Fuzzy and Neuro-Fuzzy admission control approaches are examined to meet performance constraints of call blocking probabilities and to optimize wireless channel utilization. A simple Fuzzy Logic controller is first implemented that allows the admission controller to make decisions about new calls attempting to access the network. This work is then extended to a completely adaptive Neuro-Fuzzy Controller. The Neuro-Fuzzy admission controller allows for the training and adaptive learning of the arrival and service profile of calls in each cell. An analytical model is developed for use as the initial training set of the Neuro-Fuzzy admission controller. An algorithm that allows for the updating of the neural network as data is collected is also developed. The proposed model allows the Neuro-Fuzzy admission controller to handle different cell dwell time distributions. This makes the admission controller more applicable to more realistic cell distributions and highly adaptive to coverage areas which are not covered by current models. Finally, the same Neuro-Fuzzy logic controller is extended to allow more variables to be considered (such as user velocity and cell dwell time). The results obtained show that the original Neuro-Fuzzy controller handles the simulated scenarios without the need for more complex extensions.

02Chapter1.pdf (363 kB)
03Chapter2.pdf (471 kB)
04Chapter3.pdf (506 kB)
05Chapter4.pdf (3312 kB)
06Chapter5.pdf (6431 kB)
07Chapter6.pdf (522 kB)
08Chapter7.pdf (693 kB)
09Chapter8.pdf (314 kB)
10References.pdf (330 kB)