A Class of Call Admission Control Algorithms for Resource Management and Reward Optimization for Servicing Multiple QoS Classes in Wireless Networks and Its Applications

We develop and analyze a class of CAC algorithms for resource management in wireless networks with the goal not only to satisfy QoS constraints, but also to maximize a value or reward objective function specified by the system. We demonstrate through analytical modeling and simulation validation that the CAC algorithms developed in this research for resource management can greatly improve the system reward obtainable with QoS guarantees, when compared with existing CAC algorithms designed for QoS satisfaction only.

We design hybrid partitioning-threshold, spillover and elastic CAC algorithms based on the design techniques of partitioning, setting thresholds and probabilistic call acceptance to use channel resources for servicing distinct QoS classes. For each CAC algorithm developed, we identify optimal resource management policies in terms of partitioning or threshold settings to use channel resources. By comparing these CAC algorithms head-to-head under identical conditions, we determine the best algorithm to be used at runtime to maximize system reward with QoS guarantees for servicing multiple service classes in wireless networks.

We study solution correctness, solution optimality and solution efficiency of the class of CAC algorithms developed. We ensure solution optimality by comparing optimal solutions achieved with those obtained by ideal CAC algorithms via exhaustive search. We study solution efficiency properties by performing complexity analyses and ensure solution correctness by simulation validation based on real human mobility data. Further, we analyze the tradeoff between solution optimality vs. solution efficiency and suggest the best CAC algorithm used to best tradeoff solution optimality for solution efficiency, or vice versa, to satisfy the system's solution requirements. Moreover, we develop design principles that remain applicable despite rapidly evolving wireless network technologies since they can be generalized to deal with management of 'resources' (e.g., wireless channel bandwidth), 'cells' (e.g., cellular networks), "connections" (e.g., service calls with QoS constraints), and "reward optimization" (e.g., revenue optimization in optimal pricing determination) for future wireless service networks.

To apply the CAC algorithms developed, we propose an application framework consisting of three stages: workload characterization, call admission control, and application deployment. We demonstrate the applicability with the optimal pricing determination application and the intelligent switch routing application.