Performance Analysis of the Intelligent Mobility Optimization CRRM Approach Using a Markovian Chain Model

Due to the increasing demand of wireless services, mobile technology has rapidly progressed towards the fourth generation (4G) networking paradigm. This generation will be heterogeneous in nature and it can be achieved through the integration of different Radio Access Technologies (RATs) over a common platform. Common Radio Resource Management (CRRM) was proposed to manage radio resource utilization in heterogeneous wireless networks and to provide required Quality of Service (QoS) for allocated calls. RAT selection algorithms are an integral part of the CRRM algorithms. Their role is to decide, when a new or Vertical Handover (VHO) call is requested, which of the available RATs is most suitable to fit the need of the incoming call and when to admit them. This paper extends our earlier work on the proposed intelligent hybrid mobility optimization RAT selection approach which allocates users in high mobility to the most suitable RAT and proposes an analytical presentation of the proposed approach in a multi- dimensional Markov chain model. A comparison for the performance of centralized load-balancing, distributed and the proposed intelligent mobility optimization algorithms is presented in terms of new calls blocking probability, VHO calls dropping probability, users' satisfactions probability, average networks load and average system throughput. Simulation and analytical results show that the proposed algorithm performs better than the centralized load- balancing and distributed algorithms.