Implementation and test results of on‐channel repeater for ATSC 3.0 systems

Ahn, Sungjun; Kwon, Soon-Bark; Kwon, Hyuk‐Jun; Kim, Youngsu; Lee, Jaekwon; Shin, Yoo-Sang; Park, Sung‐Ik

doi:10.4218/etrij.2021-0194

Cited by 10 publications

(2 citation statements)

References 50 publications

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“…This paper employs the digital terrestrial television broadcasting channel model for experiments with computer simulations, as cited in [13,20]. This channel named RC20 represents a scenario where the signal is exclusively received through the Rician fading with 20 reflected paths where it guarantees a stable direct path from a main transmitter.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Accordingly, some intricate decoding algorithms have been proposed to ensure reliable decoding of the wake-up bits at a low signal-to-noise ratio (SNR) levels [10,11]. In recent times, a machine learning method based on neural networks (NN) has garnered significant attention in the field of wireless communication [12][13][14][15][16][17]. In particular, the references [18][19][20] cover reception method based on baseband signal.…”

Section: Introductionmentioning

confidence: 99%

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

Song,

Baek,

Bae

et al. 2024

Sensors

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With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment.

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Section: Simulation Results and Discussionmentioning

confidence: 99%