Timing Synchronization Based on Supervised Learning of Spectrogram for OFDM Systems
Shun Kojima, Yuta Goto, Kazuki Maruta, Shinya Sugiura, Chang Jun Ahn · IEEE Transactions on Cognitive Communications and Networking · 2023
This paper proposes a supervised convolutional neural network (CNN) based symbol timing synchronization method using the spectrogram image for preamble-less orthogonal frequency division multiplexing (OFDM) systems. With the development of mobile terminals, OFDM has become an increasingly widespread fundamental technology for wireless communications. While OFDM can achieve high-speed transmission, it is sensitive to synchronization timing for decoding. Thus, the accurate synchronization timing estimation method has become essential for reliable communication. Conventional synchronization timing estimation methods without the preamble lack investigations of estimation accuracy under varying environments, comprehensive performance evaluation, and robustness to Doppler shift. Focusing on the spectrum fluctuations observed when synchronization errors occur, our proposed approach is to train the CNN using spectrogram images to find accurate synchronization points even in noisy and fluctuating environments. The simulation results show that the proposed method achieves better synchronization accuracy than other existing methods. Furthermore, it shows the near-optimal bit error rate (BER) characteristics and superior processing time for BER in an environment close to realistic settings, such as broader synchronization timing and various Doppler shifts.