Incremental Learning-based MIMO Relay Selection

Ankit Gupta*, Mathini Sellathurai, Venkata V. Mani, Tharmalingam Ratnarajah

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

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The forthcoming 6G wireless networks are expected to be much more machine-intelligent in resource allocation, including relay selections to serve ever-increasing users and the internet of things with extended coverage. Selecting an optimal multiple-input multiple-output (MIMO) relay using conventional methods becomes challenging due to dependency on perfect channel information, which exponentially increases feedback overhead. In this paper, we propose a novel incremental learning-based online MIMO relay selection algorithm, with only imperfect channel gain information available at the relay nodes in the framework of MIMO two-way amplify-and-forward (TWAF) relay networks. In particular, we develop naive Bayes, logistic regression, and support vector-based incremental learning classifiers for the near-optimal online relay selection. Using simulated results, we show that the proposed online relay selection approaches outperform the best conventional Gram-Schmidt algorithm while reducing the feedback overhead up to a factor of eight.

Original languageEnglish
Article number3
JournalCEUR Workshop Proceedings
Publication statusPublished - 17 Aug 2022
Event1st International Workshop on Artificial Intelligence in Beyond 5G and 6G Wireless Networks 2022 - Padova, Italy
Duration: 21 Jul 2022 → …


  • amplify-and-forward
  • Incremental learning
  • MIMO
  • online relay selection
  • relay networks
  • two-way

ASJC Scopus subject areas

  • Computer Science(all)


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