Cell selection using distributed Q-learning in heterogeneous networks

Toshihito Kudo, Tomoaki Ohtsuki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

Cell selection with cell range expansion (CRE) that is a technique to expand a pico cell range virtually by adding a bias value to the pico received power, instead of increasing transmit power of the pico base station (PBS), can make coverage, cell-edge throughput, and overall network throughput improved. Many studies about CRE have used a common bias value among all user equipments (UEs), while the optimal bias values that minimize the number of UE outages vary from one UE to another. The optimal bias value that minimizes the number of UE outages depends on several factors such as the dividing ratio of radio resources between macro base stations (MBSs) and PBSs, it is given only by the trial and error method. In this paper, we propose a scheme to select a cell by using Q-learning algorithm where each UE learns which cell to select to minimize the number of UE outages from its past experience independently. Simulation results show that, compared to the practical common bias value setting, the proposed scheme reduces the number of UE outages and improves network throughput in the most cases. Moreover, instead of the degradation of the performances, it also solves the storage problem of our previous work.

Original languageEnglish
Title of host publication2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
DOIs
Publication statusPublished - 2013
Event2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 - Kaohsiung, Taiwan, Province of China
Duration: 2013 Oct 292013 Nov 1

Other

Other2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
CountryTaiwan, Province of China
CityKaohsiung
Period13/10/2913/11/1

Fingerprint

Heterogeneous networks
Outages
Throughput
Base stations
Learning algorithms
Macros
Degradation

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

Cite this

Kudo, T., & Ohtsuki, T. (2013). Cell selection using distributed Q-learning in heterogeneous networks. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013 [6694368] https://doi.org/10.1109/APSIPA.2013.6694368

Cell selection using distributed Q-learning in heterogeneous networks. / Kudo, Toshihito; Ohtsuki, Tomoaki.

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013. 6694368.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kudo, T & Ohtsuki, T 2013, Cell selection using distributed Q-learning in heterogeneous networks. in 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013., 6694368, 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013, Kaohsiung, Taiwan, Province of China, 13/10/29. https://doi.org/10.1109/APSIPA.2013.6694368
Kudo T, Ohtsuki T. Cell selection using distributed Q-learning in heterogeneous networks. In 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013. 6694368 https://doi.org/10.1109/APSIPA.2013.6694368
Kudo, Toshihito ; Ohtsuki, Tomoaki. / Cell selection using distributed Q-learning in heterogeneous networks. 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013. 2013.
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