Online Learning to Precode for FDD Massive MIMO Systems

Daeun Kim, H. Vincent Poor, Namyoon Lee

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

Abstract

This paper presents a novel multi-user precoding strategy for frequency-division duplexing massive multiple-input multiple-output downlink systems with rate-limited feedback. Inspired by a multi-armed bandit framework, our approach is to adaptively learn the best precoding action that provides the highest sum-throughput without explicit channel state information feedback. In particular, we present an online learning algorithm to find the best optimal precoding action in a timely manner, called fast upper confidence bound (Fast-UCB) precoding. The key idea is to use a fast-exploration and exploitation with pruning strategies to speed up learning rates in identifying the optimal precoding action. From simulations, we show that the proposed algorithm significantly outperforms the existing online learning algorithms, including the conventional UCB method, in the cumulative regret. In addition, we demonstrate that the Fast-UCB method achieves a higher net sum-throughput than greedy action selection with full-exploration under a short channel coherence time environment, even with much less feedback.

Original languageEnglish (US)
Title of host publication2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728173078
DOIs
StatePublished - Dec 2020
Event2020 IEEE Globecom Workshops, GC Wkshps 2020 - Virtual, Taipei, Taiwan, Province of China
Duration: Dec 7 2020Dec 11 2020

Publication series

Name2020 IEEE Globecom Workshops, GC Wkshps 2020 - Proceedings

Conference

Conference2020 IEEE Globecom Workshops, GC Wkshps 2020
CountryTaiwan, Province of China
CityVirtual, Taipei
Period12/7/2012/11/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Software

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