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dc.contributor.supervisor Cai, Jun (Electrical and Computer Engineering) en_US
dc.contributor.author Cao, Huijin
dc.date.accessioned 2018-09-12T20:10:23Z
dc.date.available 2018-09-12T20:10:23Z
dc.date.issued 2018-08-20 en_US
dc.date.submitted 2018-08-20T23:39:04Z en
dc.identifier.uri http://hdl.handle.net/1993/33335
dc.description.abstract When wireless decision-making entities have complete information, the network control problems are frequently addressed in the model-based paradigm. However, due to the practical limitation of information incompleteness/locality, directly applying the model-based solutions will face difficulties. As a result, the method of controlling-by-learning without the need for the a priori network model, namely, the model-free learning, has been considered as one promising implementation approach to wireless networks. In this thesis, the applications of the model-free learning in three networks with different characteristics and objectives are investigated, they are an opportunistic spectrum access (OSA) network, a cloudlet-based mobile cloud computing (MCC) network, and an energy harvesting based network. Effective and efficient model-free learning mechanisms in these networks are aimed to be designed. Both analytical and numerical results validate the efficacy of the proposed algorithms. en_US
dc.subject Cognitive radio networks en_US
dc.subject Opportunistic spectrum access en_US
dc.subject Nash equilibrium en_US
dc.subject Stochastic learning automata en_US
dc.subject Cloudlet-based mobile cloud computing en_US
dc.subject Energy harvesting en_US
dc.subject Proactive caching en_US
dc.subject Post-decision state en_US
dc.subject Approximate reinforcement learning en_US
dc.subject Markov decision process en_US
dc.title Applications of model-free learning in wireless networks en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.contributor.examiningcommittee Alfa, Attahiru (Electrical and Computer Engineering) Peng, Qingjin (Mechanical Engineering) Wong, Vincent (Electrical and Computer Engineering, The University of British Columbia) en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US
dc.description.note October 2018 en_US


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