QoS-aware resource allocation in wireless communication systems

With the rapid growth in demand for wireless communications, service providers are expected to provide always-on, seamless and ubiquitous wireless data services to a large number of users with different applications and different Quality of Service (QoS) requirements. The multimedia traffic is envisioned to be a concurrent mix of real-time traffic and non-real-time traffic. However, radio spectrum is a scarce resource in wireless communications. In order to adapt to the changing wireless channel conditions and meet the diverse QoS requirements, efficient and flexible packet scheduling algorithms play an increasingly important role in radio resource management (RRM). Much of the published work in RRM has focused on exploiting multi-user and multi-channel diversities. In this thesis, we adopt an adaptive cross layer approach to exploit multi-application diversity in single-carrier communication systems and additionally, multi-bit diversity in multi-carrier communication systems. Efficient and practical resource allocation (RA) algorithms with finer scheduling granularity and increased flexibility are developed to meet QoS requirements. Specifically, for single-carrier communication systems, we develop RA algorithms with flow and user multiplexing while jointly considering physical-layer time-varying channel conditions as well as application-layer QoS requirements. For multi-carrier communication systems, we propose a bitQoS-aware RA framework to adaptively match the QoS requirements of the user application bits to the characteristics of the narrowband channels. The performance gains achievable from the proposed bitQoS-aware RA framework are demonstrated with suboptimal algorithms using water-filling and bit-loading approaches. Efficient algorithms to obtain optimal and near-optimal solutions to the joint subcarrier, power and bit allocation problem with continuous and discrete rate adaptation, respectively, are developed. The increased control signaling that may be incurred, as well as the computational complexity as a result of the finer scheduling granularity, are also taken into consideration to establish the viability of the proposed RA framework and algorithms for deployment in practical networks. The results show that the proposed framework and algorithms can achieve a higher system throughput with substantial performance gains in the considered QoS metrics compared to RA algorithms that do not take QoS requirements into account or do not consider multi-application diversity and/or multi-bit diversity.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat