|dc.description.abstract||The future Heterogeneous Wireless Network (HWN) is composed of multiple Radio Access
Technologies (RATs), therefore new Radio Resource Management (RRM) schemes
and mechanisms are necessary to benefit from the individual characteristics of each RAT
and to exploit the gain resulting from jointly considering the whole set of the available
radio resources in each RAT. These new RRM schemes have to support mobile users
who can access more than one RAT alternatively or simultaneously using a multi-mode
terminal. An important RRM consideration for overall HWN stability, resource utilization,
user satisfaction, and Quality of Service (QoS) provisioning is the selection of the
most optimal and promising Access Network (AN) for a new service request. The RRM
mechanism that is responsible for selecting the most optimal and promising AN for a
new service request in the HWN is called the initial Access Network Selection (ANS).
This thesis explores the issue of ANS in the HWN. Several ANS solutions that attempt
to increase the user satisfaction, the operator benefits, and the QoS are designed, implemented,
The thesis first presents a comprehensive foundation for the initial ANS in the H\VN.
Then, the thesis analyses and develops a generic framework for solving the ANS problem
and any other similar optimized selection problem. The advantages and strengths of the
developed framework are discussed. Combined Fuzzy Logic (FL), Multiple Criteria
Decision Making (MCDM) and Genetic Algorithms (GA) are used to give the developed
framework the required scalability, flexibility, and simplicity.
The developed framework is used to present and design several novel ANS algorithms
that consider the user, the operator, and the QoS view points. Different numbers of
RATs, MCDM tools, and FL inference system types are used in each algorithm. A
suitable simulation models over the HWN with a new set of performance evolution
metrics for the ANS solution are designed and implemented. The simulation results show
that the new algorithms have better and more robust performance over the random, the service type, and the terminal speed based selection algorithms that are used as reference
algorithms. Our novel algorithms outperform the reference algorithms in- terms of the
percentage of the satisfied users who are assigned to the network of their preferences and
the percentage of the users who are assigned to networks with stronger signal strength.
The new algorithms maximize the operator benefits by saving the high cost network
resources and utilizing the usage of the low cost network resources. Usually better
results are achieved by assigning the weights using the GA optional component in the