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A Deep Learning-Based Emergency Alert Wake-Up Signal Detection Method for the UHD Broadcasting System.

Jin-Hyuk Song^1,2, Myung-Sun Baek², Byungjun Bae²

¹Department of Information and Communication Engineering, Sejong University, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)

|July 13, 2024

Summary

This study introduces a deep learning method to detect emergency alert wake-up signals without needing an Advanced Television Systems Committee (ATSC) 3.0 demodulator. This innovation simplifies disaster alerts for ultra-high definition television broadcasting.

Keywords:

ATSC 3.0 UHD broadcasting deep learning emergency alert wake-up signal detection

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Area of Science:

Broadcasting Technology
Signal Processing
Artificial Intelligence

Background:

Increasing disaster frequency necessitates advanced emergency alert systems.
Ultra-high definition (UHD) broadcasting via ATSC 3.0 offers early alert capabilities.
Conventional emergency signal detection requires complex ATSC 3.0 demodulators.

Purpose of the Study:

To propose a novel deep learning method for detecting emergency wake-up signals.
To eliminate the need for ATSC 3.0 demodulators in emergency alert detection.
To simplify the emergency alert signal detection process.

Main Methods:

Utilized a deep neural network (DNN) for bootstrap signal detection.
Employed a convolutional neural network (CNN) for wake-up signal demodulation.
Applied deep learning in the time domain, bypassing traditional signal processing steps like FFT.

Main Results:

Successfully detected emergency wake-up signals without an ATSC 3.0 demodulator.
Eliminated the necessity for Fast Fourier Transform (FFT), frequency synchronization, and interleaving.
Verified performance using real-world ATSC 3.0 radio frequency signals and a Software-Defined Radio (SDR) platform.

Conclusions:

The proposed deep learning method offers an efficient alternative for emergency alert detection.
This approach simplifies the hardware requirements for receiving emergency alerts on UHD TVs.
The method holds promise for enhancing the reliability and accessibility of disaster warning systems.