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Non-convex optimizations in wireless communications systems.
Non-convexity is the major challenge in dealing with many optimization problems arising from the analysis and design of wireless communication systems. It stems from not only the objective function of an optimization problem, such as the non-convex utility function in the wireless resource allocation problem, but also its constraints, such as the integer...
Non-convexity is the major challenge in dealing with many optimization problems arising from the analysis and design of wireless communication systems. It stems from not only the objective function of an optimization problem, such as the non-convex utility function in the wireless resource allocation problem, but also its constraints, such as the integer constraints in the subcarrier allocation of a multi-cell OFDMA network. In this thesis, we study the non-convex optimization of wireless communication systems, with emphasis on designing efficient algorithms for various practical problems in wireless systems. This thesis can be divided into two parts. The first part of this thesis is on the cross-layer optimization and interference management of wireless networks. We study how to optimize the network-wise performance (in terms of the utility function) by horizontally enforcing collaborations among different wireless nodes, and vertically performing resource allocations across different layers, e.g., joint power and subcarrier allocation. In the second part of this thesis, we propose efficient decoding algorithms for several important classes of linear block codes, such as the low-density parity-check code and the turbo product code. The convergence and optimality of the proposed decoding algorithms are analyzed; and their performance and complexity are compared with the state-of-the-art decoding algorithms such as the sum product decoder. Conclusions and extensions are included in the final chapter of this thesis.
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