Downlink Beamforming Prediction in MISO System Using Meta Learning and Unsupervised Learning

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


Various machine learning methods have been applied in downlink beamforming to yield performance gain. However, a prevalent limitation among these approaches is their reliance on substantial quantities of labeled data with high training complexity, especially when the number of users and antenna increases. In this paper, we propose two methodologies designed to mitigate these limitations. Our methods depart from conventional practices by training models using unlabeled data while concurrently curtailing the data volume required for effective training. Moreover, to reduce training complexity, we adopt an approach, based on the WMMSE and MLBF algorithm, that decomposes the beamforming vector prediction into lower-dimensional components. To enhance the adaptivity, we incorporate Rayleigh and Rician channels during the training. And we conducted experiments to train models under different amounts of data and assess model performance under conditions where testing distributions align with or diverge from the training distribution. Both methodologies show better performances than WMMSE at high SNR under the same distribution. Furthermore, models trained through unsupervised learning showcase heightened generality when confronted with novel environments characterized by divergent data distributions. Additionally, our meta learning approach yields commendable performance even with limited data availability.

Original languageEnglish
Title of host publication2023 IEEE 23rd International Conference on Communication Technology
Subtitle of host publicationAdvanced Communication and Internet of Things, ICCT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9798350325959
Publication statusPublished - 2023
Event23rd IEEE International Conference on Communication Technology, ICCT 2023 - Wuxi, China
Duration: 20 Oct 202322 Oct 2023

Publication series

NameInternational Conference on Communication Technology Proceedings, ICCT
ISSN (Print)2576-7844
ISSN (Electronic)2576-7828


Conference23rd IEEE International Conference on Communication Technology, ICCT 2023


  • downlink beamforming
  • meta learning
  • unsupervised learning
  • WSR maximization


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