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，通信工程师高度重视基于人工神经网络（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，无监督训练因无需传输导频符号即可实现自适应而备受关注。本文提出了一种新型基于ANN的无监督均衡器及其可训练现场可编程门阵列（FPGA）实现方案。我们证明，所设计的自定义损失函数使ANN能够适应变化的信道条件，性能接近有监督基线方法。此外，作为迈向实用通信系统的第一步，我们设计了所提算法的高效FPGA实现方案，其吞吐量可达吉比特/秒量级，大幅超越高性能图形处理器（GPU）。