We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexity properties can be derived. However, since the underlying factor graph contains cycles, the sum-product algorithm (SPA) yields a suboptimal algorithm. In this paper, we develop and evaluate efficient strategies to improve the performance of the factor graph-based symbol detection by means of neural enhancement. In particular, we consider neural belief propagation and generalizations of the factor nodes as an effective way to mitigate the effect of cycles within the factor graph. By applying a generic preprocessor to the channel output, we propose a simple technique to vary the underlying factor graph in every SPA iteration. Using this dynamic factor graph transition, we intend to preserve the extrinsic nature of the SPA messages which is otherwise impaired due to cycles. Simulation results show that the proposed methods can massively improve the detection performance, even approaching the maximum a posteriori performance for various transmission scenarios, while preserving a complexity which is linear in both the block length and the channel memory.

翻译：我们考虑应用系数图框架在线性符号间干扰信道中检测符号。根据Ungerboeck观察模型,可以得出具有吸引力复杂特性的检测算法。但是,由于基本系数图包含周期,总产品算法(SPA)产生一个亚最佳算法。在本文件中,我们制定和评价有效的战略,通过神经增强手段改进基于系数图的符号探测性能。特别是,我们认为神经信仰传播和系数节点的概括化是减轻系数图内循环效应的有效方法。通过对频道输出应用通用预处理器,我们提出一种简单的技术,以改变每个源要素图的每个源值。使用这种动态系数图转换,我们打算保持因循环而受损的SPA电文的外在性质。模拟结果表明,拟议的方法可以大大改进检测性,甚至接近各种传输情景的后部性表现,同时保持块长度和频道记忆的线性复杂性。