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Related Experiment Videos

Generative Adversarial Network-Based Joint Mapping and Localization for Millimeter-Wave Communication Systems.

Zexu Zhao¹, Zhigang Chen¹, Lu Chen¹

¹School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)

|July 15, 2026

Summary

A new generative adversarial network (GAN) method improves joint localization and mapping (JLAM) in millimeter-wave (mmWave) systems using angle difference of arrival (ADOA) measurements. This approach significantly reduces localization error and enhances room boundary estimation.

Keywords:

angle difference of arrival (ADOA)deep neural network generative adversarial network (GAN)joint localization and mapping (JLAM)millimeter-wave communication systems

Related Experiment Videos

Area of Science:

Wireless Communication
Artificial Intelligence
Robotics and Navigation

Background:

Millimeter-wave (mmWave) systems offer high bandwidth but require precise localization and mapping.
Existing joint localization and mapping (JLAM) methods face challenges in accuracy and efficiency.
Angle difference of arrival (ADOA) measurements provide rich spatial information for localization.

Purpose of the Study:

To propose a novel generative adversarial network (GAN)-based JLAM method utilizing ADOA measurements for mmWave systems.
To enhance the accuracy of mobile terminal (MT) localization and indoor map estimation.
To improve upon existing JLAM algorithms in terms of localization error and room boundary estimation.

Main Methods:

Developed a GAN-based JLAM method with a deep auto-encoder discriminator.
Modeled the generator as an explicit geometric ADOA function, not a black-box neural network.
Exploited 2D distribution of high-dimensional ADOA vectors to learn data distribution and AP topology.

Main Results:

Achieved an average localization error of approximately 0.25 m under representative simulation conditions.
Demonstrated a significant error reduction of about 58% compared to the JADE algorithm (0.60 m error).
Showcased more accurate room boundary estimation compared to the JADE algorithm.

Conclusions:

The proposed GAN-based JLAM method is effective for mmWave communication systems.
The method accurately estimates MT positions and indoor maps by recovering AP topology.
This novel approach offers superior performance in localization accuracy and map detail over existing methods.