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Related Experiment Video

Updated: Aug 2, 2025

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Deep-Learning-Based Antenna Alignment Prediction for Mobile Indoor Communication.

Árpád László Makara1, Botond Tamás Csathó1, András Rácz2

  • 1Department of Broadband Infocommunications and Electromagnetic Theory, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.

Sensors (Basel, Switzerland)
|April 13, 2023
PubMed
Summary
This summary is machine-generated.

Future indoor wireless networks can use mmWave frequencies. A new deep neural network method predicts optimal beam directions for moving users, improving signal reception in challenging propagation environments.

Keywords:
antenna beam alignmentdeep learningmmWavemobile indoor communicationray tracing

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Wireless Communications

Background:

  • Millimeter wave (mmWave) frequency bands offer significant potential for future indoor wireless networks.
  • Restricted propagation conditions in mmWave bands necessitate advanced techniques like beamforming and beam management (tracking, prediction).
  • Artificial intelligence (AI) offers a promising approach to learn spatial channel patterns for beam prediction.

Purpose of the Study:

  • To present a novel deep-neural-network-based method for predicting optimal beam directions in mmWave indoor wireless networks.
  • To address the challenge of beam management for moving users by leveraging learned spatial propagation patterns.
  • To enable a user-side antenna management system for enhanced reception.

Main Methods:

  • Development of a deep neural network with memory capabilities.
  • Utilizing the network to learn hidden spatial propagation patterns of the indoor wireless channel.
  • Predicting the optimal reception beam direction for the next time step based on learned patterns.

Main Results:

  • The proposed method effectively predicts the best reception directions for moving users.
  • The highest expected signal level at the next moment was used as the criterion for the best direction.
  • Evaluation using three distinct metrics demonstrated the method's predictive accuracy and usability.

Conclusions:

  • The deep-neural-network-based approach provides a viable solution for beam management in mmWave indoor networks.
  • The method enhances user-side antenna management by predicting optimal beam directions.
  • This innovation contributes to more robust and efficient future wireless communication systems.