Cross Layer Scheduling And Resource Allocation Algorithms For Cellular Wireless Networks

This thesis considers the problem of cross layer scheduling and radio resource allocation of multiple users in the downlink of time-slotted and frequency-slotted cellular data networks. For these networks, opportunistic scheduling algorithms improve system performance by exploiting time variations of the radio channel. Within the broader framework of opportunistic scheduling, this thesis solves three distinct problems and proposes efficient and scalable solutions for them. First, we present novel optimal and approximate opportunistic scheduling algorithms that combine channel fluctuation and user mobility information in their decision rules. The algorithms propose the use of dynamic fairness constraints. These fairness constraints adapt according to the user mobility. The optimal algorithm is an off-line algorithm that precomputes constraint values according to a known mobility model. The approximate algorithm is an on-line algorithm that relies on the future prediction of the user mobility locations in time. We show that the use of mobility information increases channel capacity. We also provide analytical bounds on the performance of the approximate algorithm. Second, this thesis presents a new opportunistic scheduling solution that maximizes the aggregate user performance subject to certain minimum and maximum performance constraints. By constraining the performance experienced by individual users, who share a common radio downlink, to some upper bounds, it is possible to provide the system operator with a better control of radio resource allocations and service differentiation among different classes of users. The proposed solution offers better performance than existing solution under practical channel conditions. Finally, we present a dynamic subcarrier allocation solution for fractional frequency reuse in multicell orthogonal frequency division multiple access systems. We formulate the subcarrier allocation as an equivalent set partitioning problem and then p.