Radio Resource Management and Metric Estimation for Multicarrier CDMA systems

It is envisaged that the 4 1h generation (4G) of wireless networks will need to carry a variety of heterogeneous traffic types with different Quality of Service (QoS) requirements. A number of Medium Access Control (MAC) and Physical (PRY) layer technologies have been proposed for 4G networks. These schemes are typically based on multicarrier transmission and require a large amount of scarce radio frequency spectrum relative to current systems. As a consequence, there has been increased interest in dynamic radio resource allocation (RRA) algorithms that aim to make decisions on the optimal usage of resources to provide the QoS required by 40 networks. The majority of RRA algorithms manage resources on a per layer basis and either make assumptions about the nature of traffic and propagation conditions, or assume that they have perfect, real-time estimates of these conditions. In practice a per layer management of resources may offer poorer performance benefits when compared to a cross layer approach. In addition, the assumptions made about the traffic and propagation conditions mean that it is hard to take full advantage of the variability in these conditions. These issues are further compounded for multicarrier systems as such systems have an additional degree of freedom by virtue of their frequency component. This thesis investigates the management of radio resources in the PHY and MAC layers of multicarrier CDMA (MC-CDMA) systems and how the estimation of metrics in the various layers may be used in performing a cross layer management of resources to provide increased Q0S whilst making optimal usage of the radio resource. At the PHY layer, the grouping and subcarrier allocation problem for a grouped MC-CDMA system is formulated as an integer linear programming problem. Two algorithms are proposed to solve this problem, namely a Branch and Bound based algorithm and a mixed greedy-probabilistic Local Search algorithm. The Local Search algorithm is found to offer increased QoS (in terms of BER) for more users at a lower complexity than any of the other algorithms. At the MAC layer, a new multi-rate model multi-group MC-CDMA (MG-MC-CDMA) is introduced and the performance of power control and multi-group allocation algorithms in the MG-MC-CDMA system examined. A generalised processor sharing scheduler that takes advantage of the particular features of the MG-MC-CDMA system is proposed. The performance of some of these MAC layer algorithms is found to be limited by their assumptions as to how much capacity is available for use, underscoring the desirability of accurate capacity estimates to enable the management of resources. A capacity model, incorporating an interference analysis and that takes into account the nature of the traffic types carried in the system, is outlined. In addition to MAC layer metrics characterising the traffic in the system, the capacity model has, as some of its required metrics, PHY layer parameters such as the ratio of inter-cell interference to total received power and information of whether or not a mobile is in a cell's edge region. New techniques are proposed to estimate these metrics. It is shown that the resulting dynamic capacity estimation framework can accurately and dynamically measure the capacity. The final contribution of the thesis is the use of the proposed dynamic capacity estimation framework to develop new radio resource management algorithms that work across the PRY and MAC layers to deliver enhanced QoS. Declaration of originality I hereby declare that the research recorded in this thesis and the thesis itself was composed and originated entirely by myself in the Institute for Digital Communications at the University of Edinburgh. The software programs used to perform the simulations were written by myself with the following exceptions: . The routines used to generate Gaussian distributed noise and uniformly distributed samples were obtained from Numerical Recipes in C [1]. . The routines used to perform fast Fourier transforms were obtained from the "Fastest Fourier Transform in the West (FFT%'V)" project [2]. . The source code for the maximum likelihood multi-user detector used in Chapter 3 was provided by Dr. Emad Al-Susa. . The routines for the simplex algorithm utilised in Chapter 3 were obtained from the "LP Solve" project [3].