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.

翻译：在通信系统中，自编码器（AE）指用人工神经网络（ANN）替代发射机和接收机部分功能，通过信道模型实现系统端到端训练的概念。该方法旨在提升通信性能，尤其适用于动态信道条件，但代价是训练与推理需承担高计算复杂度。现场可编程门阵列（FPGA）已被证明是能效型ANN实现的理想平台。然而，ANN的高运算量和庞大规模限制了其在资源受限设备上的性能表现，这对低延迟、高吞吐量通信系统至关重要。针对这一挑战，我们提出一种基于ANN的高效FPGA解映射方法，融合了AE的自适应能力与传统解映射算法的效率优势。在适应信道条件后，提取ANN隐式学习的信道特征，从而在推理阶段使用经优化的传统解映射算法。通过FPGA实现结果及与传统系统的通信性能对比，验证了该方法的硬件效率。本研究为ANN通信算法在FPGA上的实际应用开辟了道路。