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Analyzing a supported beam under unsymmetrical loadings is essential in structural engineering to understand how beams respond to varied force distributions. This analysis involves calculating the deflection and identifying points where the slope of the beam is zero, which are crucial for ensuring structural stability and functionality.
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BeamNet: Unsupervised Beamforming for ISAC Systems Under Imperfect CSI.

Helitha Nimnaka1, Samiru Gayan1, Ruhui Zhang2

  • 1Department of Electronic and Telecommunication Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka.

Entropy (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces BeamNet, an unsupervised deep learning method for integrated sensing and communication (ISAC) beamforming. BeamNet effectively balances communication and sensing rates, even with imperfect channel information.

Keywords:
Nakagami-m fadingbeamformingimperfect CSIintegrated sensing and communication (ISAC)unsupervised deep learning

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Integrated Sensing and Communication (ISAC) systems merge radar sensing and wireless communication for enhanced efficiency.
  • Transmitting beamforming is crucial for optimizing performance in dual-function systems.
  • Existing methods often require perfect channel state information (CSI) or complex optimization solvers.

Purpose of the Study:

  • To propose BeamNet, an unsupervised deep learning framework for transmit beamforming in ISAC systems.
  • To enable joint optimization of communication rate (CR) and sensing rate (SR) under general fading and imperfect CSI.
  • To learn the CR-SR Pareto frontier without requiring beamforming labels or embedded solvers.

Main Methods:

  • Developed BeamNet, an unsupervised deep learning framework mapping noisy channel estimates to beamforming vectors.
  • Trained BeamNet end-to-end by maximizing a weighted sum of CR and SR.
  • Evaluated performance in Rayleigh, Nakagami-m, and Rician fading channels with varying CSI quality.

Main Results:

  • BeamNet accurately reproduced analytical Pareto-optimal solutions in perfect CSI scenarios.
  • Characterized CR-SR trade-offs across different fading parameters and assessed robustness to distribution mismatch.
  • Demonstrated superior performance under imperfect CSI compared to closed-form beamformers, recovering performance loss from estimation errors.

Conclusions:

  • Unsupervised learning provides a flexible and robust approach for ISAC beamforming in fading environments.
  • BeamNet effectively handles imperfect channel state information, offering a practical solution for future wireless networks.
  • The framework learns the CR-SR trade-off efficiently, outperforming traditional methods in challenging conditions.