In recent years data-driven machine learning approaches have been extensively studied to replace or enhance traditionally model-based processing in digital communication systems. In this work, we focus on equalization and propose a novel neural network (NN-)based approach, referred to as SICNN. SICNN is designed by deep unfolding a model-based iterative soft interference cancellation (SIC) method. It eliminates the main disadvantages of its model-based counterpart, which suffers from high computational complexity and performance degradation due to required approximations. We present different variants of SICNN. SICNNv1 is specifically tailored to single carrier frequency domain equalization (SC-FDE) systems, the communication system mainly regarded in this work. SICNNv2 is more universal and is applicable as an equalizer in any communication system with a block-based data transmission scheme. Moreover, for both SICNNv1 and SICNNv2, we present versions with highly reduced numbers of learnable parameters. Another contribution of this work is a novel approach for generating training datasets for NN-based equalizers, which significantly improves their performance at high signal-to-noise ratios. We compare the bit error ratio performance of the proposed NN-based equalizers with state-of-the-art model-based and NN-based approaches, highlighting the superiority of SICNNv1 over all other methods for SC-FDE. Exemplarily, to emphasize its universality, SICNNv2 is additionally applied to a unique word orthogonal frequency division multiplexing (UW-OFDM) system, where it achieves state-of-the-art performance. Furthermore, we present a thorough complexity analysis of the proposed NN-based equalization approaches, and we investigate the influence of the training set size on the performance of NN-based equalizers.

翻译：近年来，数据驱动的机器学习方法被广泛研究，以替代或增强数字通信系统中传统的基于模型的信号处理。本文聚焦于均衡问题，提出了一种新型神经网络方法，命名为SICNN。SICNN通过深度展开基于模型的迭代软干扰消除（SIC）方法设计而成，消除了其基于模型对应方案的主要缺陷，即因必要近似而导致的高计算复杂度和性能退化。我们提出了SICNN的不同变体。SICNNv1专门针对单载波频域均衡（SC-FDE）系统设计，这是本文主要关注的通信系统。SICNNv2更为通用，适用于任何采用基于块数据传输方案的通信系统作为均衡器。此外，对于SICNNv1和SICNNv2，我们还提出了具有大幅减少可学习参数数量的版本。本文的另一贡献是一种为基于NN的均衡器生成训练数据集的新方法，该方法显著提升了在高信噪比下的性能。我们将所提出的基于NN的均衡器的误码率性能与最先进的模型基和NN基方法进行了比较，凸显了SICNNv1在SC-FDE系统中相对于所有其他方法的优越性。为体现其通用性，我们还将SICNNv2应用于独特字正交频分复用（UW-OFDM）系统，并取得了最先进的性能。此外，我们对所提出的基于NN的均衡方法进行了全面的复杂度分析，并研究了训练集大小对基于NN的均衡器性能的影响。