Sanjabi Boroujeni, Maziar2015-04-232015-04-232014-12http://hdl.handle.net/11299/171713University of Minnesota Ph.D. dissertation.December 2014. Major: Electrical Engineering. Advisor: Zhi-Quan Luo. 1 computer file (PDF); ix, 123 pages, appendices A-D.In this dissertation we consider the central task of resource management in wireless Heterogeneous Networks (HetNets). Resource management plays an important role in satisfying the increasing need for wireless data in HetNets. Our emphasis is mainly on cross layer strategies. Various aspects of cross layer resource management can be formulated as optimization problems. Throughout this dissertation, we use advanced optimization techniques to develop algorithms that are capable of efficiently solving these optimization problems. First, we consider the joint base station assignment and linear {transceiver} design problem. In order to gain a better understanding of resource management problems, we analyze the complexity of solving the resulting optimization problem. We establish the NP-hardness of this problem for a wide range of system-wide utility functions.Due to the fundamental difficulty of globally solving these problems, our emphasis in the rest of this dissertation is on devising efficient algorithms that can approximately solve these problems under different practical limitations. One major practical limitation of current resource management strategies is the need for the channel state information at the transmitter side. In this thesis we consider transceiver design in wireless HetNet when the channel state information is incomplete/inexact. We propose a general stochastic successive upper-bound minimization approach to optimize the average/ergodic utility of the system. We specialize our method to obtain an efficient stochastic sum-rate maximization algorithm. The proposed algorithm can use the statistical knowledge instead of actual channel values and is guaranteed to converge to the set of stationary points of the stochastic sum-rate maximization problem. We further generalize our stochastic method to a cross layer framework for jointly optimizing the base station clustering and the downlink beamformers in a partial coordinated transmission scenario. The partial coordination is crucial in improving the overall system performance by reducing backhaul overhead. We validate the effectiveness of our methods via numerical experiments.enAlgorithmsHeterogeneous networksOptimizationResource managementStochastic optimizationWireless communicationsElectrical engineeringThesis or Dissertation