Deep Learning-Based Bootstrap Detection Scheme for Digital Broadcasting System

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.