On optimization of the resource allocation in multi-cell networks.

Chen, Jieying.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 58-62).Abstract in English only.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Motivation --- p.1Chapter 1.2 --- Literature Review --- p.5Chapter 1.3 --- Contributions Of This Thesis --- p.7Chapter 1.4 --- Structure Of This Thesis --- p.8Chapter 2 --- Problem Formulation --- p.9Chapter 2.1 --- The JBAPC Problem --- p.9Chapter 2.2 --- The Single-Stage Reformulation --- p.12Chapter 3 --- The BARN Algorithm --- p.15Chapter 3.1 --- Preliminary Mathematics --- p.15Chapter 3.1.1 --- Duality Of The Linear Optimization Problem --- p.15Chapter 3.1.2 --- Benders Decomposition --- p.18Chapter 3.2 --- Solving The JBAPC Problem Using BARN Algorithm --- p.21Chapter 3.3 --- Performance And Convergence --- p.24Chapter 3.3.1 --- Global Convergence --- p.26Chapter 3.3.2 --- BARN With Error Tolerance --- p.26Chapter 3.3.3 --- Trade-off Between Performance And Convergence Time --- p.26Chapter 4 --- Accelerating BARN --- p.30Chapter 4.1 --- The Relaxed Master Problem --- p.30Chapter 4.2 --- The Feasibility Pump Method --- p.32Chapter 4.3 --- A-BARN Algorithm For Solving The JBAPC Problem --- p.34Chapter 5 --- Computational Results --- p.36Chapter 5.1 --- Global Optimality And Convergence --- p.36Chapter 5.2 --- Average Convergence Time --- p.37Chapter 5.3 --- Trade-off Between Performance And Convergence Time --- p.38Chapter 5.4 --- Average Algorithm Performance Of BARN and A-BARN --- p.39Chapter 6 --- Discussions --- p.47Chapter 6.1 --- Resource Allocation In The Uplink Multi-cell Networks --- p.47Chapter 6.2 --- JBAPC Problem In The Uplink Multi-cell Networks --- p.48Chapter 7 --- Conclusion --- p.50Chapter 7.1 --- Conclusion Of This Thesis --- p.50Chapter 7.2 --- Future Work --- p.51Chapter A --- The Proof --- p.52Chapter A.l --- Proof of Lemma 1 --- p.52Chapter A.2 --- Proof of Lemma 3 --- p.55Bibliography --- p.5

An Optimization Theoretical Framework for Resource Allocation over Wireless Networks

With the advancement of wireless technologies, wireless networking has become ubiquitous owing to the great demand of pervasive mobile applications. Some fundamental challenges exist for the next generation wireless network design such as time varying nature of wireless channels, co-channel interferences, provisioning of heterogeneous type of services, etc. So how to overcome these difficulties and improve the system performance have become an important research topic. Dynamic resource allocation is a general strategy to control the interferences and enhance the performance of wireless networks. The basic idea behind dynamic resource allocation is to utilize the channel more efficiently by sharing the spectrum and reducing interference through optimizing parameters such as the transmitting power, symbol transmission rate, modulation scheme, coding scheme, bandwidth, etc. Moreover, the network performance can be further improved by introducing diversity, such as multiuser, time, frequency, and space diversity. In addition, cross layer approach for resource allocation can provide advantages such as low overhead, more efficiency, and direct end-to-end QoS provision. The designers for next generation wireless networks face the common problem of how to optimize the system objective under the user Quality of Service (QoS) constraint. There is a need of unified but general optimization framework for resource allocation to allow taking into account a diverse set of objective functions with various QoS requirements, while considering all kinds of diversity and cross layer approach. We propose an optimization theoretical framework for resource allocation and apply these ideas to different network situations such as: 1.Centralized resource allocation with fairness constraint 2.Distributed resource allocation using game theory 3.OFDMA resource allocation 4.Cross layer approach On the whole, we develop a universal view of the whole wireless networks from multiple dimensions: time, frequency, space, user, and layers. We develop some schemes to fully utilize the resources. The success of the proposed research will significantly improve the way how to design and analyze resource allocation over wireless networks. In addition, the cross-layer optimization nature of the problem provides an innovative insight into vertical integration of wireless networks