Bandit-based relay selection in cooperative networks over unknown stationary channels

Nikolaos Nomikos, Sadegh Talebi, Risto Wichman, Themistoklis Charalambous

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)
60 Downloads (Pure)

Abstract

In recent years, wireless node density has increased rapidly, as more base stations, users, and machines coexist. Exploiting this node density, cooperative relaying has been deployed to improve connectivity throughout the network. Such a configuration, however, often demands relay scheduling, which comes with increased channel estimation and signaling overheads. To reduce these overheads, in this paper, we propose low-complexity relay scheduling mechanisms with the aid of a multi-armed bandit (MAB) framework. More specifically, this MAB framework is used for relay scheduling, based only on observing the acknowledgements/negative-acknow-ledgements (ACK/NACK) of packet transmissions. Hence, a bandit-based opportunistic relay selection (BB - ORS) mechanism is developed, recovering eventually the performance of classical opportunistic relay selection (0RS) when channel state information (CSI) is available without requiring any CSI. In addition, a distributed implementation of BB - ORS is presented, herein called d - BB - ORS, where distributed timers are used at the relays for relay selection, thus reducing the signaling overhead significantly. BB - ORS is compared to optimal scheduling with full CSI and the negligible performance gap is compensated by the low-complexity low-overhead implementation, while it surpasses the performance of ORS with outdated CSI.

Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing, MLSP 2020
PublisherIEEE
ISBN (Electronic)9781728166629
DOIs
Publication statusPublished - Sep 2020
MoE publication typeA4 Article in a conference publication
EventIEEE International Workshop on Machine Learning for Signal Processing - Espoo, Finland
Duration: 21 Sep 202024 Sep 2020
Conference number: 30
https://ieeemlsp.cc

Publication series

NameIEEE International Workshop on Machine Learning for Signal Processing
ISSN (Print)2161-0363
ISSN (Electronic)2161-0371

Workshop

WorkshopIEEE International Workshop on Machine Learning for Signal Processing
Abbreviated titleMLSP
Country/TerritoryFinland
CityEspoo
Period21/09/202024/09/2020
Internet address

Keywords

  • Machine learning
  • Multi-armed bandits
  • Relay selection
  • Upper confidence bound policies

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