Spectrum Allocation in Networks with Finite Sources and Data-Driven Characterization of Users\u27 Stochastic Dynamics

During emergency situations, the public safety communication systems (PSCSs) get overloaded with high traffic loads. Note that these PSCSs are finite source networks. The goal of our study is to propose techniques for an efficient allocation of spectrum in finite source networks that can help alleviate the overloading of PSCSs. In a PSCS, there are two system segments, one for the system-access control and the other for communications, each having dedicated frequency channels. The first part of our research, consisting of three projects, is based on modeling and analysis of finite source systems for optimal spectrum allocation, for both access-control and communications. In the first project, Chapter 2, we study the allocation of spectrum based on the concept of cognitive radio systems. In the second project, Chapter 3, we study the optimal communication channel allocation by call admission and preemption control. In the third project, Chapter 4, we study the optimal joint allocation of frequency channels for access-control and communications. Note that the aforementioned spectrum allocation techniques require the knowledge of the call traffic parameters and the priority levels of the users in the system. For practical systems, these required pieces of information are extracted from the call records meta-data. A key fact that should be considered while analyzing the call records is that the call arrival traffic and the users priority levels change with a change in events on the ground. This is so because a change in events on the ground affects the communication behavior of the users in the system, which affects the call arrival traffic and the priority levels of the users. Thus, the first and the foremost step in analyzing the call records data for a given user, for extracting the call traffic information, is to segment the data into time intervals of homogeneous or stationary communication behavior of the user. Note that such a segmentation of the data of a practical PSCS is the goal of our fourth project, Chapter 5, which constitutes the second part of our study

Preemption control of multi-class loss networks

This thesis addresses the analysis and optimization of preemption in multi-class loss networks. Preemption, admission control and rate adaptation, are control mechanisms that enable loss network operators to provide quality of service (QoS) guarantees for admitted calls. This research includes two parts: i) performance characterization of a two parallel link loss network servicing multiple classes of calls under a speci c preemption and admission policy, and ii) preemption and admission control policy analysis for a single loss link servicing two classes of calls.In Part I, we consider a two parallel link multi-class loss network, where a call may preempt, if necessary, any calls with lower priorities and may in turn be preempted by any calls with higher priorities. The preemption policy permits both preemption from a preferred link to a backup link if possible, and eviction from either link if necessary. Our contributions in this part include: i) characterizing the rates of each class causing preemption of active lower priority calls, and therates of each class being preempted by an arriving higher priority call in Erlang-B functions when all classes share a common service rate; ii) simple expressions of these preemption rates through uniform asymptotic approximation; and iii) asymptotic approximation of these preemption rates using nearly completely decomposable (NCD) Markov chain techniques when classes have individual service rates.After analyzing the performance of a typical policy, we would also like to study various policies. In Part II, we analyze di erent preemption and admission control policies for a two-class loss link where per-class revenue is earned per unit time for each active call, and an instantaneous preemption cost is incurred whenever the preemption mechanism is employed. Our contributions in this part include: i) showing that under reasonable reward models, if we always preempt when the link is full, then it is better not to preempt at non-full states; ii) a su cient condition under which the average revenue of optimal preemption policy without admission control exceeds that of optimal admission control policy without preemption, which are established via policy improvement theorems fromstochastic dynamic programming.Ph.D., Computer Engineering -- Drexel University, 201