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 multidimensional 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 loadbalancing and distributed algorithms. © 2014 ACADEMY PUBLISHER

Modeling PCS networks under general call holding time and cell residence time distributions

Abstract—In a personal communication service (PCS) network, the call completion probability and the effective call holding times for both complete and incomplete calls are central parameters in the network cost/performance evaluation. These quantities will depend on the distributions of call holding times and cell residence times. The classical assumptions made in the past that call holding times and cell residence times are exponentially distributed are not appropriate for the emerging PCS networks. This paper presents some systematic results on the probability of call completion and the effective call holding time distributions for complete and incomplete calls with general cell residence times and call holding times distributed with various distributions such as Gamma, Erlang, hyperexponential, hyper-Erlang, and other staged distributions. These results provide a set of alternatives for PCS network modeling, which can be chosen to accommodate the measured data from PCS field trials. The application of these results in billing rate planning is also discussed. Index Terms—Billing rate planning, call blocking, call holding time, call termination, cell residence, handoff, PCS. I

Generalized Session Models for Wireless Cellular Networks : Communication Networks

This contribution deploys generalized basic relations of session models, which remain completely independent of the possible underlying technologies. First, a renewal session trajectory model is proposed and yields a mean value theorem. This confirms that the mean number of handoffs remains insensitive with respect to the density distributions of the participating residence and session times with vanishing forced terminations. In contrast to rather complicated and pure transform domain solutions of the past a hybrid original-transform-domain approach is proposed. This keeps the relations physically transparent and facilitates the consideration of handoff blockings and forced terminations. Second, a further theorem based on an appropriate inversion of the transform domain densities shows that the state probabilities of handoffs may be expressed in an explicit symbolic form if generic Gamma distributed session and residence time durations are assumed. Third, an estimated mean Diameter protocol rate for generally distributed session and residence times including forced terminations proves to be given in an explicit form too. Finally, keying moments and complementary distribution functions of generalized handoff process are symbolically derived and enumerated

STOCHASTIC MODELING AND TIME-TO-EVENT ANALYSIS OF VOIP TRAFFIC

Voice over IP (VoIP) systems are gaining increased popularity due to the cost effectiveness, ease of management, and enhanced features and capabilities. Both enterprises and carriers are deploying VoIP systems to replace their TDM-based legacy voice networks. However, the lack of engineering models for VoIP systems has been realized by many researchers, especially for large-scale networks. The purpose of traffic engineering is to minimize call blocking probability and maximize resource utilization. The current traffic engineering models are inherited from the legacy PSTN world, and these models fall short from capturing the characteristics of new traffic patterns. The objective of this research is to develop a traffic engineering model for modern VoIP networks. We studied the traffic on a large-scale VoIP network and collected several billions of call information. Our analysis shows that the traditional traffic engineering approach based on the Poisson call arrival process and exponential holding time fails to capture the modern telecommunication systems accurately. We developed a new framework for modeling call arrivals as a non-homogeneous Poisson process, and we further enhanced the model by providing a Gaussian approximation for the cases of heavy traffic condition on large-scale networks. In the second phase of the research, we followed a new time-to-event survival analysis approach to model call holding time as a generalized gamma distribution and we introduced a Call Cease Rate function to model the call durations. The modeling and statistical work of the Call Arrival model and the Call Holding Time model is constructed, verified and validated using hundreds of millions of real call information collected from an operational VoIP carrier network. The traffic data is a mixture of residential, business, and wireless traffic. Therefore, our proposed models can be applied to any modern telecommunication system. We also conducted sensitivity analysis of model parameters and performed statistical tests on the robustness of the models’ assumptions. We implemented the models in a new simulation-based traffic engineering system called VoIP Traffic Engineering Simulator (VSIM). Advanced statistical and stochastic techniques were used in building VSIM system. The core of VSIM is a simulation system that consists of two different simulation engines: the NHPP parametric simulation engine and the non-parametric simulation engine. In addition, VSIM provides several subsystems for traffic data collection, processing, statistical modeling, model parameter estimation, graph generation, and traffic prediction. VSIM is capable of extracting traffic data from a live VoIP network, processing and storing the extracted information, and then feeding it into one of the simulation engines which in turn provides resource optimization and quality of service reports