On End-to-End Learning of Joint Detection and Decoding for Short-Packet Communications

We propose a joint detection and decoding scheme for short-packet wireless communications in scenarios that require to first detect the presence of a message before actually decoding it. For this, we extend the recently proposed serial Turbo-autoencoder neural network (NN) architecture and train it to find short messages that can be, all “at once detected, synchronized, equalized and decoded when sent over an unsynchronized channel with memory. The conceptional advantage of the proposed system stems from a holistic message structure with superimposed pilots for joint detection and decoding without the need of relying on a dedicated preamble. This results not just in a higher spectral efficiency, but also translates into shorter messages and potentially lower latency when compared to the usage of a dedicated preamble. We compare the detection error rate (DER), bit error rate (BER) and block error rate (BLER) performance of the proposed system with a hand-crafted state-of-the-art conventional baseline. Our performance simulations show a significant advantage of the proposed autoencoder-based system over the conventional baseline in every scenario up to messages conveying $k=96$ information bits.