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Reinforcement Learning with Imitation for Cavity Filter Tuning: Solving problems by throwing DIRT at them
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Cavity filters are vital components of radio base stations and networks.After production, they need tuning, which has proven to be a difficultprocess to do manually and even more so to automate. Previously, attemptsto automate this process with Reinforcement Learning have beenmade but have failed to reach consistent performance on anything butthe simplest filter models. This Master thesis builds upon these resultsand aims to improve them. Multiple methods are tested and evaluated,including introducing a pre-processing step, tuning hyperparameters anddividing the problem into multiple sub-tasks. In particular, by using Imitationlearning as an initial phase, a semi-realistic filter model with 13tuning screws is tuned, fulfilling both insertion loss and return loss requirements.On this problem, this algorithm has a greater efficiency thanany previously published results on Reinforcement Learning for Cavityfilter tuning.

Abstract [sv]

Kavitetsfilter är vitala komponenter i radiobasstationer och -nätverk. Efterproduktion behöver de trimmas, vilket har visat sig vara svårt att göramanuellt och ännu svårare att automatisera. Det har tidigare gjorts försökatt automatisera denna process med Reinforcement Learning, mendessa har misslyckats att nå konsekvent prestanda på något annat ände allra enklaste filtermodeller. Detta examensarbete bygger på dessa resultatoch ämnar att skapa bättre resultat utifrån dessa. Flera metodertestas och utvärderas för detta, vilka inkluderar att förbehandla datan,att trimma hyperparametrar och att dela upp problemet i flera underproblem.I synnerhet uppnås högre effektivitetsgrad på halvrealistiskafiltermodeller med 13 skruvar, när både genomsläppsförluster och returförlusterbetraktas, än några andra publicerade resultat inom områdetgenom att utnyttja imitationsinlärning som en initialiseringsfas.

Place, publisher, year, edition, pages
2019. , p. 59
Series
TRITA-EECS-EX ; 2019:310
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-254422OAI: oai:DiVA.org:kth-254422DiVA, id: diva2:1332077
External cooperation
Ericsson
Educational program
Master of Science - Systems, Control and Robotics
Supervisors
Examiners
Available from: 2019-06-27 Created: 2019-06-27 Last updated: 2019-06-27Bibliographically approved

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CiteExportLink to record
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