Equalization is an important task at the receiver side of a digital wireless communication system, which is traditionally conducted with model-based estimation methods. Among the numerous options for model-based equalization, iterative soft interference cancellation (SIC) is a well-performing approach since error propagation caused by hard decision data symbol estimation during the iterative estimation procedure is avoided. However, the model-based method suffers from high computational complexity and performance degradation due to required approximations. In this work, we propose a novel neural network (NN-)based equalization approach, referred to as SICNN, which is designed by deep unfolding of a model-based iterative SIC method, eliminating the main disadvantages of its model-based counterpart. We present different variants of SICNN. SICNNv1 is very similar to the model-based method, and is specifically tailored for single carrier frequency domain equalization systems, which is the communication system we regard in this work. The second variant, SICNNv2, is more universal, and is applicable as an equalizer in any communication system with a block-based data transmission scheme. We highlight the pros and cons of both variants. Moreover, for both SICNNv1 and SICNNv2 we present a version with a highly reduced number of learnable parameters. We compare the achieved 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. Also, 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：均衡是数字无线通信系统接收端的一项重要任务，传统上采用基于模型的估计方法进行处理。在众多基于模型的均衡方案中，迭代软干扰消除因其能够避免迭代估计过程中硬判决数据符号估计导致的错误传播，表现出优异的性能。然而，基于模型的方法因所需的近似处理而存在计算复杂度高和性能下降的问题。本研究提出一种新的基于神经网络的均衡方法，称为SICNN，其通过深度展开基于模型的迭代SIC方法设计而成，消除了基于模型方法的主要缺陷。我们展示了SICNN的不同变体。SICNNv1与基于模型方法高度相似，专门针对本文所考虑的单载波频域均衡系统设计。第二种变体SICNNv2更具通用性，可作为任何采用基于块数据传输方案的通信系统中的均衡器。我们强调了两者的优缺点。此外，针对SICNNv1和SICNNv2，我们还提出一种可学习参数数量大幅缩减的版本。我们将所提出的基于神经网络的均衡器实现的误码率性能与最先进的基于模型和基于神经网络的方法进行比较，凸显SICNNv1在所有方法中的优越性。同时，我们对所提出的基于神经网络的均衡方法进行全面复杂度分析，并研究训练集规模对基于神经网络的均衡器性能的影响。