Training Deep Filters for Physical-Layer Frame Synchronization

• Published in: IEEE open journal of the Communications Society Vol. 3; pp. 1063 - 1075
• Main Authors: Kalade, Sarunas, Crockett, Louise H., Stewart, Robert W.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Communications systems; Convolutional neural networks; Correlation; Deep learning; frame synchronization; fully convolutional neural network; Internet of Things; Receivers; Signal processing; Symbols; Synchronism; Synchronization; Training; Wireless communication; Wireless sensor networks
• ISSN: 2644-125X; 2644-125X
• DOI: 10.1109/OJCOMS.2022.3185973

In this paper we demonstrate the application of Fully Convolutional Neural Network (FCN) for Frame Synchronization (FS) in bursty single carrier transmissions, commonly used in wireless sensor networks and Internet of Things (IoT) applications. Our approach shows greatly improved performance compared to noncoherent correlation-based methods under carrier phase and frequency offsets, especially for shorter preambles. Using a fully convolutional architecture allows the training of a deep filter, which we believe is more suited to signal processing tasks than more commonly used deep learning architectures with fully connected layers. In terms of deployment within a wider communications system, it could be treated similarly to a typical signal processing filter, which means it can be deployed to inputs of arbitrary length. Additionally, because the proposed model is composed only of convolutional layers, the entire model benefits from the weight sharing property of convolutional filters, and results in a greatly reduced memory footprint compared to that of similar models containing fully connected layers.