Optimisation of future mobile communication systems using deep learning

Jakob Thrane

Research output: Book/ReportPh.D. thesisResearch

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Abstract

Mobile communication networks are complex systems consistingof many incredible engineering achievements. The current andfuture mobile communication systems are, and in the future even more so, complex to manage and optimize. Sub-optimal mobile networks result in cost-ineffective deployments causing poor customer experience and increased operational and capital expenses for operators. Deep Learning (DL) have in the recent years provided with impressive results in complex tasks such as speech recognition and computer vision. The complex reality of mobile communication systems is expected to increase, and Deep Learning has been hailed as a necessary component co overcome suchchallenges. The content of this dissertation is the exploration of novel methodologies available in Deep Learning toolbox. This dissertation has resulted in several advancements for applying Deep Learning to complex tasks in mobile communication systems. Accurately, three novel methods are presented. 1) Accurate signal quality predictions in unseen locations withlow data complexity using geographical images. 2) Significant improvements to channel estimation applied on sparse reference signals in uplink and 3) An adaptive reinforcement learning algorithm capable of avoiding contamination in the radio environment. In addition to this, several study items are presented.Most noticeably the outcome can be summarized as, 1) Complex ray-tracing methods show little to no performance gain compared to simple empirical models for mobile communication propagation modelling. 2) Current deep-indoor propagation models show poor generalized performance and require novel solutions.
Original languageEnglish
PublisherTechnical University of Denmark
Number of pages270
Publication statusPublished - 2020

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