Conference paper Open Access

Dynamic Functional Split Selection in Energy Harvesting Virtual Small Cells Using Temporal Difference Learning

Temesgene, Dagnachew, A.; Miozzo, Marco; Dini, Paolo


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  <dc:creator>Temesgene, Dagnachew, A.</dc:creator>
  <dc:creator>Miozzo, Marco</dc:creator>
  <dc:creator>Dini, Paolo</dc:creator>
  <dc:date>2018-09-09</dc:date>
  <dc:description>Flexible functional split in Cloud Radio Access Network (CRAN) is a promising approach to overcome the capacity and latency challenges in the fronthaul. In such architecture, the baseband processing takes place partially at local base stations and the remaining processes are executed at the central cloud. On the other hand, we have seen a recent trend of powering base stations with ambient energy sources to achieve both environmental sustainability and profit advantages. As the base stations become smaller and deployed in densified manner, it is evident that baseband processing power consumption has a huge share in the total base station power consumption breakdown. Given that such base stations are powered by energy harvesting sources, energy availability conditions the decision on where to place each baseband function in the system. This work focuses on applying reinforcement learning techniques, in particular Q-learning and SARSA, for optimal placement of baseband functional split options in virtualized small cells that are solely powered by energy harvesting sources. In addition, a comparison of such online optimization solution with respect to offline performance bounds is provided.</dc:description>
  <dc:description>grant TEC2017-88373-R (5G-REFINE). © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.</dc:description>
  <dc:identifier>https://zenodo.org/record/2525718</dc:identifier>
  <dc:identifier>10.1109/PIMRC.2018.8580970</dc:identifier>
  <dc:identifier>oai:zenodo.org:2525718</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/675891/</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>energy harvesting</dc:subject>
  <dc:subject>virtual small cells</dc:subject>
  <dc:subject>functional split</dc:subject>
  <dc:subject>CRAN</dc:subject>
  <dc:subject>Reinforcement learning</dc:subject>
  <dc:subject>SARSA</dc:subject>
  <dc:subject>Q-learning</dc:subject>
  <dc:subject>temporal difference</dc:subject>
  <dc:title>Dynamic Functional Split Selection in Energy Harvesting Virtual Small Cells Using Temporal Difference Learning</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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