Learning Joint Synchronization, Equalization, and Decoding in Short Packet Communications

Abstract

Abstract The rapid evolution of cellular communication technologies necessitates improvements to support emerging applications like autonomous driving and remote medical surgery. Ultra-Reliable Low Latency Communications (URLLC), a key scenario in 5G, demands stringent latency and reliability, with even more rigorous requirements expected in 6G. Traditional communication systems using dedicated preambles for detection, synchronization, and channel estimation is suboptimal for short packet transmissions, highlighting the need for innovative approaches. This thesis investigates the potential of deep learning (DL) techniques in enhancing short packet communications. By designing an autoencoder-based joint synchronization, equalization, and decoding scheme, the system jointly learns the transmitter and receiver end-to-end for the tasks of synchronization, equalization, and decoding without relying on a dedicated preamble. The objectives include developing an autoencoder-based communication scheme, extending it for joint equalization and decoding, and proposing a joint synchronization, equalization, and decoding scheme under block fading waveform channels. The findings demonstrate that an end-to-end learning approach using a convolutional neural network-autoencoder (CNN-AE) improves spectral efficiency and reduces overhead in short packet communications while maintaining system reliability. The proposed system, without using dedicated preambles, outperforms the nonasymptotic achievability bound for pilot-assisted transmission systems in terms of block error rate (BLER) at high signal-to-noise ratios (SNRs). This highlights the potential of DL techniques in addressing the challenges of short packet communications in future wireless networks.