One critical step to successfully integrate wireless data networks to the high-speed wired backbone is the design of network control policies that efficiently utilize resources to provide Quality of Service (QoS) to the users in the integrated networks. Such a design has remained a challenge since wireless networks are time-varying in nature, not only in terms of user/packet arrivals but also in terms of physical channel conditions and access opportunities. In this thesis, we study the stochastic control of time-varying networks to design efficient scheduling and resource allocation policies.;In Chapter 4, we go into further detail and consider the problem of network control from a new perspective through which we carefully incorporate the time-efficiency of underlying scheduling algorithms. Specifically, we develop a policy that dynamically adjusts the time given to the available scheduling algorithms according to queue-backlog and channel correlations. We study the resulting stability region of developed policy and show that the region is at least as large as the one for any static policy.;Finally, motivated by the current under-utilization of wireless spectrum, in Chapter 5, we investigate the control of cognitive radio networks as a special example of networks that provide time-varying access opportunities. We assume that users dynamically join and leave the network and may have different utility functions, or could collaborate for a common purpose. We develop a policy that performs joint admission and resource control and works for any user load, either inside or outside the capacity region. Through Lyapunov Optimization techniques, we show that the developed policy can achieve a utility performance arbitrarily close to the optimality with a tradeoff in the average service delay of admitted users.;In particular, in Chapter 3, we focus on a broad class of control policies that work based on a pick-and-compare principle for networks with time-varying channels. By trading the throughput for complexity and memory requirement, these policies require less complexity compared to the well-investigated throughput-optimal Generalized Maximum Weight Matching (GMWM) policy and also require only linear-memory storage with the number of dataflows. Through Lyapunov analysis tools, we characterize the stability region and delay performance of the studied policies and show how they vary in response to the channel variations.
展开▼
