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Title: Transmission power control in wireless networks
Author: Liao, Rui
ISNI:       0000 0004 2684 8237
Awarding Body: Queen Mary, University of London
Current Institution: Queen Mary, University of London
Date of Award: 2010
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Ad hoc wireless networks have emerged as a promising communication scheme to meet the ever growing portability and infrastructureless demand of wireless services. The transmission power level affects signal quality and interference which causes congestion and thus impacts the communication performance. Hence, power control has been the focus of extensive research. In this thesis, we examine the problem of power control in wireless networks, specially in ad hoc wireless networks. Two important types of power control, which are power control with fixed SNIR targets and power control with variable SNIR targets, are discussed in the thesis. We first introduce some important techniques and results involved in the development of power control algorithms and give literature review. A PI power control approach from literature is introduced. Due to lack of stability analysis, we show there are problems in the existing algorithm. We then propose a stable Proportional-integral (PI) power control algorithm. A forgetting factor is adopted to improve the transient performance. Distributed power control algorithms for systems with fixed SNIR targets might diverge when the feasibility condition is not satisfied. Multi-objective optimisation (MO) is adopted to deal with power control with variable SNIR targets. After discussing the existing MO algorithm, we propose a quadratic multiobjective-optimisation (QMO) algorithm where a quadratic objective function and the greedy methodology are adopted for the dynamics. Theoretical and simulation results of convergence of the new algorithms are given. We also provide review of some important power control frameworks which can be used to show convergence of power control algorithms. However, the QMO algorithm does not fall into any existing framework. In order to show convergence of the QMO algorithm, we suggest a new generalised framework in this thesis.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Materials Science ; Engineering