Model Based Reinforcement Learning for Automatic Tuning of Cavity Filters

Model Based Reinforcement Learning for Automatic Tuning of Cavity Filters

As telecommunication continues developing, the demand for mass production of well calibrated Base Transceiver Stations (BTS) components increases. Cavity Filters are an essential piece of every BTS; however, manufacturing tolerances often lead to detuned filters which require costly post-production...

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Bibliographic Details
Main Author: Nimara, Doumitrou Daniil
Format: Others
Language:English
Published: KTH, Skolan för elektroteknik och datavetenskap (EECS) 2021
Subjects:
Reinforcement Learning
Cavity Filter Tuning
Sample Complexity
Background Planning
Förstärkande inlärning
inställning av kavitetsfilter
provkomplexitet
bakgrundsplanering
Computer and Information Sciences
Data- och informationsvetenskap
Online Access:http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-304021

Internet

http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-304021

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