In recent years, communication engineers put strong emphasis on artificial neural network (ANN)-based algorithms with the aim of increasing the flexibility and autonomy of the system and its components. In this context, unsupervised training is of special interest as it enables adaptation without the overhead of transmitting pilot symbols. In this work, we present a novel ANN-based, unsupervised equalizer and its trainable field programmable gate array (FPGA) implementation. We demonstrate that our custom loss function allows the ANN to adapt for varying channel conditions, approaching the performance of a supervised baseline. Furthermore, as a first step towards a practical communication system, we design an efficient FPGA implementation of our proposed algorithm, which achieves a throughput in the order of Gbit/s, outperforming a high-performance GPU by a large margin.

翻译：近年来，通信工程师高度关注基于人工神经网络（ANN）的算法，旨在提升系统及其组件的灵活性和自主性。在此背景下，无监督训练具有特殊意义，因为它无需传输导频符号的开销即可实现自适应。本文提出了一种新颖的基于ANN的无监督均衡器及其可训练现场可编程门阵列（FPGA）实现。我们证明，所设计的自定义损失函数能使ANN适应变化的信道条件，性能接近有监督基准方法。此外，作为迈向实用通信系统的第一步，我们实现了所提算法的高效FPGA设计，其吞吐量达吉比特/秒量级，性能显著优于高性能GPU。