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Related Experiment Video

Updated: May 28, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

State-Driven Adaptive Deep-Unfolded PGA Algorithm for Hybrid Beamforming in MIMO-JCAS Systems.

Fulai Liu¹, Zihao Wang², Yan Gao²

¹Laboratory of Electromagnetic Environment Cognition and Control Utilization, and Hebei Key Laboratory of Marine Perception Network and Data Processing, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

This study introduces a novel state-driven adaptive deep-unfolded hybrid beamforming algorithm for massive MIMO joint communication and sensing (JCAS) systems. The method optimizes hybrid beamforming for better performance and faster convergence.

Keywords:

adaptive hyperparameter control deep-unfolded network hybrid beamforming joint communication and sensing

Related Experiment Videos

Last Updated: May 28, 2026

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Gain-compensation Methodology for a Sinusoidal Scan of a Galvanometer Mirror in Proportional-Integral-Differential Control Using Pre-emphasis Techniques

Published on: April 4, 2017

Area of Science:

Wireless communication systems
Signal processing
Optimization algorithms

Background:

Hybrid beamforming (HBF) offers a balance between performance and cost in massive MIMO joint communication and sensing (JCAS) systems.
Designing HBF for MIMO-JCAS is challenging due to coupled precoders and high-dimensional search spaces.

Purpose of the Study:

To propose a state-driven adaptive deep-unfolded HBF algorithm for MIMO-JCAS systems.
To address the challenges of coupled optimization and dynamic tradeoffs in HBF design.

Main Methods:

Redesigned analog precoder update using manifold-based form for constant-modulus constraints.
Enhanced digital precoder update with a learnable gradient-balancing mechanism.
Introduced a state-driven control network for adaptive hyperparameter scaling.

Main Results:

The proposed algorithm demonstrates improved joint objective performance compared to conventional methods.
Achieved faster convergence and enhanced robustness in simulations.
Effectively manages the dynamic communication-sensing tradeoff.

Conclusions:

The state-driven adaptive deep-unfolded HBF algorithm offers a more effective optimization strategy for MIMO-JCAS.
The adaptive approach improves performance and efficiency over static methods.
Validates the effectiveness of the proposed method through simulation results.