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dc.contributor.authorLiu, Xiao
dc.date.accessioned2019-03-12T18:18:43Z
dc.date.available2019-03-12T18:18:43Z
dc.date.issued2019-03-12T18:18:43Z
dc.identifier.urihttp://hdl.handle.net/10222/75229
dc.description.abstractIn wireless communication systems, synchronization is one of the most important issues. The requirement for synchronization is especially intensified when there is strong channel distortion. For instance, the Doppler effect can shift the carrier in the frequency domain and scale the signal in the time domain. Similarly, the multipath propagation channel poses problems to the conventional synchronization methods. This dissertation studies the synchronization techniques, including the symbol timing and carrier frequency recovery for coherent wireless receivers. The application is focused on the underwater acoustic communications, where time-varying multipath fading dominates the channel characteristics. The conventional synchronization techniques are generally derived based on the maximum likelihood principle, such that the second order statistics of the received data are utilized. However, this may not be an optimum solution in fading channels. In this work, a new entropy-based synchronization criterion is explored. Synchronization is achieved by minimizing the entropy estimated from the eye diagram and the constellation diagram. Key implementation details are addressed towards the realization of entropy based synchronization algorithms. In addition, the performance is evaluated in controlled conditions. It is shown that entropy minimization has great potential and offers certain advantages for synchronization in wireless communication, particularly for pulse shaping filters with small excess bandwidth, as well as in multipath fading channels. Furthermore, the latest deep learning technique is applied to synchronize the baseband signal. A neural network based coherent receiver is designed. Unlike the conventional receiver which consists of a series of function blocks, the neural network based receiver does not explicitly implement any function blocks. Its function is trained from end to end to achieve over all optimization. As such, this new receiver structure has the potential to outperform the conventional receivers in nonlinear or nonparametric propagation channels.en_US
dc.language.isoenen_US
dc.subjectSynchronizationen_US
dc.subjectUnderwater Acousticsen_US
dc.subjectEntropyen_US
dc.subjectDeep learningen_US
dc.titleSynchronization Techniques for Coherent Underwater Acoustic Receiversen_US
dc.date.defence2019-02-07
dc.contributor.departmentDepartment of Electrical & Computer Engineeringen_US
dc.contributor.degreeDoctor of Philosophyen_US
dc.contributor.external-examinerFrancois Gagnonen_US
dc.contributor.graduate-coordinatorDmitry Trukhacheven_US
dc.contributor.thesis-readerDavid Barclayen_US
dc.contributor.thesis-readerJacek Ilowen_US
dc.contributor.thesis-supervisorJean-François Bousqueten_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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