  In communication systems, Autoencoder (AE) refers to the concept of replacing
parts of the transmitter and receiver by artificial neural networks (ANNs) to
train the system end-to-end over a channel model. This approach aims to improve
communication performance, especially for varying channel conditions, with the
cost of high computational complexity for training and inference.
Field-programmable gate arrays (FPGAs) have been shown to be a suitable
platform for energy-efficient ANN implementation. However, the high number of
operations and the large model size of ANNs limit the performance on
resource-constrained devices, which is critical for low latency and
high-throughput communication systems. To tackle his challenge, we propose a
novel approach for efficient ANN-based remapping on FPGAs, which combines the
adaptability of the AE with the efficiency of conventional demapping
algorithms. After adaption to channel conditions, the channel characteristics,
implicitly learned by the ANN, are extracted to enable the use of optimized
conventional demapping algorithms for inference. We validate the hardware
efficiency of our approach by providing FPGA implementation results and by
comparing the communication performance to that of conventional systems. Our
work opens a door for the practical application of ANN-based communication
algorithms on FPGAs.
