Machine Learning-assisted Symbol Synchronization in Digital Communication over Fading Channels
Keywords:
Bit error rate (BER), Convolutional neural networks (CNNs), Digital communication, Fading channels, Machine learning, Supervised learning, Symbol synchronization, Timing recoveryAbstract
Symbol synchronization plays a pivotal role in ensuring accurate demodulation and data recovery in digital communication systems. However, in the presence of fading channels such as Rayleigh and Rician fading symbol timing estimation becomes highly challenging due to rapid signal distortion, multipath effects, and dynamic channel conditions. Traditional synchronization techniques, while effective in ideal or static environments, often fail to deliver reliable performance under severe fading, leading to increased bit error rates and reduced communication efficiency.
This paper proposes a machine learning-assisted approach to symbol synchronization that leverages pattern recognition and data-driven prediction to adaptively estimate timing offsets in real-time. By extracting key signal features such as envelope patterns and pilot symbol correlations, and training lightweight models like decision trees and convolutional neural networks (CNNs), the system dynamically aligns received symbols with improved resilience to channel variability. Simulation results demonstrate that the proposed ML-based synchronization method outperforms conventional correlator-based techniques in terms of timing accuracy and bit error rate across various signal-to-noise ratios. This approach offers a promising direction for robust synchronization in next-generation wireless systems, particularly for IoT and mobile communication applications operating in harsh or fast-fading environments.
References
M. K. Simon and M. Alouini, “Digital Communication over Fading Channels, 2nd ed. Hoboken, NJ, USA: Wiley, 2005.
S. M. Aldossari and K.-C. Chen, “Machine learning for wireless communication channel modeling: An overview,” Wireless Personal Communications, vol. 106, no. 1, pp. 41–70, Mar. 2019, doi: https://doi.org/10.1007/s11277-019-06275-4
H. W. Lee and S. W. Choi, “A light-weighted machine learning approach to channel estimation for new-radio systems,” Electronics, vol. 12, no. 23, pp. 4740–4740, Nov. 2023, doi: https://doi.org/10.3390/electronics12234740
Q. Bai, J. Wang, Y. Zhang and J. Song, “Deep learning-based channel estimation algorithm over time selective fading channels,” in IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 125-134, March 2020, doi: https://doi.org/10.1109/TCCN.2019.2943455
S. Hu, X. Chen, W. Ni, E. Hossain and X. Wang, “Distributed machine learning for wireless communication networks: Techniques, architectures, and applications,” in IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 1458-1493, third quarter 2021, doi: https://doi.org/10.1109/COMST.2021.3086014
S. Kojima, Y. Goto, K. Maruta, S. Sugiura and C. J. Ahn, “Timing synchronization based on supervised learning of spectrogram for OFDM systems,” in IEEE Transactions on Cognitive Communications and Networking, vol. 9, no. 5, pp. 1141-1154, Oct. 2023, doi: https://doi.org/10.1109/TCCN.2023.3280159
