Adaptive Resource Allocation for Statistical QoS Provisioning in Mobile Wireless Communications and Networks

Due to the highly-varying wireless channels over time, frequency, and space domains, statistical QoS provisioning, instead of deterministic QoS guarantees, has become a recognized feature in the next-generation wireless networks. In this dissertation, we study the adaptive wireless resource allocation problems for statistical QoS provisioning, such as guaranteeing the specified delay-bound violation probability, upper-bounding the average loss-rate, optimizing the average goodput/throughput, etc., in several typical types of mobile wireless networks. In the first part of this dissertation, we study the statistical QoS provisioning for mobile multicast through the adaptive resource allocations, where different multicast receivers attempt to receive the common messages from a single base-station sender over broadcast fading channels. Because of the heterogeneous fading across different multicast receivers, both instantaneously and statistically, how to design the efficient adaptive rate control and resource allocation for wireless multicast is a widely cited open problem. We first study the time-sharing based goodput-optimization problem for non-realtime multicast services. Then, to more comprehensively characterize the QoS provisioning problems for mobile multicast with diverse QoS requirements, we further integrate the statistical delay-QoS control techniques — effective capacity theory, statistical loss-rate control, and information theory to propose a QoS-driven optimization framework. Applying this framework and solving for the corresponding optimization problem, we identify the optimal tradeoff among statistical delay-QoS requirements, sustainable traffic load, and the average loss rate through the adaptive resource allocations and queue management. Furthermore, we study the adaptive resource allocation problems for multi-layer video multicast to satisfy diverse statistical delay and loss QoS requirements over different video layers. In addition, we derive the efficient adaptive erasure-correction coding scheme for the packet-level multicast, where the erasure-correction code is dynamically constructed based on multicast receivers’ packet-loss statuses, to achieve high error-control efficiency in mobile multicast networks. In the second part of this dissertation, we design the adaptive resource allocation schemes for QoS provisioning in unicast based wireless networks, with emphasis on statistical delay-QoS guarantees. First, we develop the QoS-driven time-slot and power allocation schemes for multi-user downlink transmissions (with independent messages) in cellular networks to maximize the delay-QoS-constrained sum system throughput. Second, we propose the delay-QoS-aware base-station selection schemes in distributed multiple-input-multiple-output systems. Third, we study the queueaware spectrum sensing in cognitive radio networks for statistical delay-QoS provisioning. Analyses and simulations are presented to show the advantages of our proposed schemes and the impact of delay-QoS requirements on adaptive resource allocations in various environments

Effective Capacity Analysis for Cognitive Networks under QoS Satisfaction

Spectrum sensing and dynamic spectrum access (DSA) techniques in cognitive radio networks (CRN) have been extensively investigated since last decade. Recently, satisfaction of quality-of-service (QoS) demands for secondary users (SU) has attracted great attention. The SU can not only discover the transmission opportunities, but also cognitively adapts the dynamic spectrum access strategies to its own QoS requirement and the environment variations. In this paper, we study how the delay QoS requirement affects the strategy on network performance. We first treat the delay-QoS in interference constrained cognitive radio network by applying the effective capacity concept, focusing on the two dominant DSA schemes: underlay and overlay. We obtain the effective capacity of the secondary network and determine the power allocation policies that maximize the throughput of the cognitive user. The underlay and overlay approaches may have their respective advantages under diverse propagation environment and system parameters. If the cognitive network can dynamically choose the DSA strategy under different environment, its performance could be further improved. We propose a selection criterion to determine whether to use underlay or overlay scheme under the given QoS constraint and the PUs’ spectrum-occupancy probability. Thus, the throughput of the CRN could be increased. Performance analysis and numerical evaluations are provided to demonstrate the effective capacity of CRN based on the underlay and the overlay schemes, taking into consideration the impact of delay QoS requirement and other related parameters