Deep learning‐based scalable and robust channel estimator for wireless cellular networks

Lee, Anseok; Kwon, Yongjin; Park, Hanjun; Lee, Heesoo

doi:10.4218/etrij.2022-0209

Cited by 3 publications

(2 citation statements)

References 24 publications

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“…Recently, the scale and frequency of disasters and accidents have increased, leading to a significant rise in damages. To enhance efficient disaster preparedness and expedite damage relief, various broadcasting and communication systems develop emergency alert technologies [1][2][3][4][5][6]. The ATSC (Advanced Television Systems Committee) 3.0 standard is defined as the state-of-the art terrestrial digital broadcasting system specification [7,8] and one of the systems that offers disaster services.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Song,

Baek,

Bae

et al. 2024

Sensors

View full text Add to dashboard Cite

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“…The paper [5] “Deep Learning‐based Scalable and Robust Channel Estimator for Wireless Cellular Networks” presents a two‐stage scalable channel estimator (TSCE) that uses a DL‐based scalable and robust channel estimator comprising two DL networks to efficiently support different resource allocation sizes and reference signal configurations. The results show that the proposed TSCE system can learn the wireless propagation channels correctly and outperform both traditional estimators and baseline DL‐based estimators.…”

mentioning

confidence: 99%