This dissertation focuses on the resource allocation problem in next-generation cellular wireless networks. Our goal is to design and evaluate algorithms and procedures to provide a balanced load and to improve the energy-efficiency of these networks, while satisfying the quality-of-service constraints of the users. The contributions of this dissertation are (i) a new handover policy to balance the load in Long Term Evolution (LTE) Heterogeneous Networks (HetNets), (ii) an analytical characterization of the efficiency and fairness trade-off of LTE uplink schedulers, and (iii) energy-efficient resource allocation algorithms for LTE HetNets with quality-of-service constraints (QoS).
First, we address the load balancing problem in HetNets deployments. We focus on the cell selection and handover policies as more base stations with different properties and functionalities are deployed. Conventional methods such as the strongest cell approach, used in single-layer network architectures, do not offer balanced loading or optimal performance for the HetNets due to the transmit power differences, backhaul, and access constraints of different base station types.
Therefore, we propose a new handover decision policy that employs cell breathing, which dynamically adjusts the cell coverage regions based on the uplink interference measurements and current system load. The proposed policy also contributes to the self-adaptive and self-organization goals of the next-generation cellular systems.
Next, we investigate the uplink resource scheduling problem in single carrier frequency-domain multiple access systems. We present an efficient implementation method that translates these scheduling problems into set partitioning problems. Then, we discuss a family of utility functions that enable us to investigate the performance of different frequency domain schedulers such as the sum-rate maximization, proportional fair, and max-min fair schedulers. We use the price of fairness as a metric to analytically quantify the efficiency and fairness trade-offs in the schedulers and provide several upper bounds. We believe that this type of analysis can provide guidelines for the network operators to control the efficiency and fairness trade-off as the data traffic grows.
Finally, we investigate the multi-cell multi-carrier network energy efficiency problem where we propose utility-based energy-efficient resource allocation algorithms. We consider a linearized load-adaptive power consumption model at the base stations. We study the interference pricing mechanisms which include the inter-cell interference contributions and penalize the transmissions based on the interference they create. We propose two types of power control algorithms. First, we propose an iterative multi-level water-filling algorithm for multi-cell wireless networks. Second, we employ the gradient ascent method to control the transmit power of base stations. Both of these frameworks are extended to include QoS constraints such as the minimum rate constraints for each user. We present the optimality conditions and convergence of both algorithms, along with their performance evaluations.
