Modelling and performance evaluation of wireless and mobile communication systems in heterogeneous environments

It is widely expected that next generation wireless communication systems will be heterogeneous, integrating a wide variety of wireless access networks. Of particular interest recently is the integration of cellular networks (GSM, GPRS, UMTS, EDGE and LTE) and wireless local area networks (WLANs) to provide complementary features in terms of coverage, capacity and mobility support. These different networks will work together using vertical handover techniques and hence understanding how well these mechanisms perform is a significant issue. In this thesis, these networks are modelled to yield performance results such as mean queue lengths and blocking probabilities over a range of different conditions. The results are then analysed using network constraints to yield operational graphs based on handover probabilities to different networks. Firstly, individual networks with horizontal handover are analysed using performability techniques. The thesis moves on to look at vertical handovers between cellular networks using pure performance models. Then the integration of cellular networks and WLAN is considered. While analysing these results it became clear that the common models that were being used were subjected to handover hysteresis resulting from feedback loops in the model. A new analytical model was developed which addressed this issue but was shown to be problematic in developing state probabilities for more complicated scenarios. Guard channels analysis, which is normally used to give priority to handover traffic in mobile networks, was employed as a practical solution to the observed handover hysteresis. Overall, using different analytical techniques as well as simulation, the results of this work form an important part in the design and development of future mobile systems

Topics in perturbation analysis for stochastic hybrid systems

Control and optimization of Stochastic Hybrid Systems (SHS) constitute increasingly active fields of research. However, the size and complexity of SHS frequently render the use of exhaustive verification techniques prohibitive. In this context, Perturbation Analysis techniques, and in particular Infinitesimal Perturbation Analysis (IPA), have proven to be particularly useful for this class of systems. This work focuses on applying IPA to two different problems: Traffic Light Control (TLC) and control of cancer progression, both of which are viewed as dynamic optimization problems in an SHS environment. The first part of this thesis addresses the TLC problem for a single intersection modeled as a SHS. A quasi-dynamic control policy is proposed based on partial state information defined by detecting whether vehicle backlogs are above or below certain controllable threshold values. At first, the threshold parameters are controlled while assuming fixed cycle lengths and online gradient estimates of a cost metric with respect to these controllable parameters are derived using IPA techniques. These estimators are subsequently used to iteratively adjust the threshold values so as to improve overall system performance. This quasi-dynamic analysis of the TLC\ problem is subsequently extended to parameterize the control policy by green and red cycle lengths as well as queue content thresholds. IPA estimators necessary to simultaneously control the light cycles and thresholds are rederived and thereafter incorporated into a standard gradient based scheme in order to further ameliorate system performance. In the second part of this thesis, the problem of controlling cancer progression is formulated within a Stochastic Hybrid Automaton (SHA) framework. Leveraging the fact that cell-biologic changes necessary for cancer development may be schematized as a series of discrete steps, an integrative closed-loop framework is proposed for describing the progressive development of cancer and determining optimal personalized therapies. First, the problem of cancer heterogeneity is addressed through a novel Mixed Integer Linear Programming (MILP) formulation that integrates somatic mutation and gene expression data to infer the temporal sequence of events from cross-sectional data. This formulation is tested using both simulated data and real breast cancer data with matched somatic mutation and gene expression measurements from The Cancer Genome Atlas (TCGA). Second, the use of basic IPA techniques for optimal personalized cancer therapy design is introduced and a methodology applicable to stochastic models of cancer progression is developed. A case study of optimal therapy design for advanced prostate cancer is performed. Given the importance of accurate modeling in conjunction with optimal therapy design, an ensuing analysis is performed in which sensitivity estimates with respect to several model parameters are evaluated and critical parameters are identified. Finally, the tradeoff between system optimality and robustness (or, equivalently, fragility) is explored so as to generate valuable insights on modeling and control of cancer progression

Toward Accessible Multilevel Modeling in Systems Biology: A Rule-based Language Concept

Promoted by advanced experimental techniques for obtaining high-quality data and the steadily accumulating knowledge about the complexity of life, modeling biological systems at multiple interrelated levels of organization attracts more and more attention recently. Current approaches for modeling multilevel systems typically lack an accessible formal modeling language or have major limitations with respect to expressiveness. The aim of this thesis is to provide a comprehensive discussion on associated problems and needs and to propose a concrete solution addressing them