Deep Learning-based Frame and Timing Synchronization for End-to-End Communications

Abstract

End-to-end learning-based communications systems are more likely to achieve the global optimal performance. In this work, we implement a complete communications system as an end-to-end deep neural network, including transmitter, channel model, synchronization and receiver. There are a lot of issues which can result to the out of synchronization between the transmitter and the receiver, including sampling timing offset, sampling frequency offset and so on. Therefore, we focus on frame and timing synchronization in the end-to-end learning-based communications systems. Our results show that such an end-to-end learning-based communications system is robust to the impairments that arise from sampling frequency offset. An extra synchronization model based on the convolutional neural networks is introduced to achieve frame synchronization and compensate for the impairments which arise from sampling time offset and sampling time error. The performances of the synchronization model are evaluated from the symbol error rate and the frame header detection results. It can correctly detect most of the actual position of frame header, and it is about 2dB better than that of the direct correlation detection in the end-to-end communications systems.