Deep Learning-Based Bootstrap Detection Scheme for Digital Broadcasting System

被引：2

作者：

Kang, Tae-Hoon ^{[1
,2
]}

Lee, Won-Seok ^{[1
,2
]}

Baek, Myung-Sun ^{[3
]}

Bae, Byungjun ^{[3
]}

Song, Hyoung-Kyu ^{[1
,2
]}

机构：

[1] Sejong Univ, Dept Informat & Commun Engn, Seoul 05006, South Korea

[2] Sejong Univ, Dept Convergence Engn Intelligent Drone, Seoul 05006, South Korea

[3] Elect & Telecommun Res Inst ETRI, Daejeon 34129, South Korea

来源：

IEEE ACCESS | 2021年 / 9卷

关键词：

Deep learning; Signal detection; Training; Receivers; Broadcasting; Time-domain analysis; Channel estimation; ATSC; 3; 0; bootstrap; deep learning; convolutional neural network; signal detection; broadcasting; CHANNEL ESTIMATION; SIGNAL-DETECTION;

D O I：

10.1109/ACCESS.2021.3051906

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the advanced television systems committee (ATSC) 3.0 system, the concept of flexibility is significant for supporting backward compatibility within the same ATSC 3.0 system. However, since the conventional bootstrap signal detection scheme is difficult to support the flexibility, the conventional bootstrap signal detection scheme should be newly designed according to the change of version. In this paper, a convolution neural network (CNN) model for bootstrap signal detection in ATSC 3.0 is proposed to maintain the flexibility of bootstrap. Additionally, for minimizing the loss of error performance of CNN-based bootstrap detection scheme, this paper proposes two dimensional alternate array to utilize the correlation of the adjacent bootstrap symbol and proposes the offline learning method using the bootstrap signal corrupted by noise to improve the error performance.

引用

页码：19562 / 19571

页数：10

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