基于因子图的符号检测的神经增强
Neural Enhancement of Factor Graph-based Symbol Detection
摘要：
研究了因子图框架在线性符号间干扰信道中符号检测的应用。循环因子图有可能产生低复杂度的符号检测器，但如果应用普遍存在的和积算法，则是次优的。在本文中，我们提出和评估的战略，以提高性能的循环因子图为基础的符号检测算法，通过神经增强。特别是，我们将神经信念传播作为一种有效的方法来抵消因子图中循环的影响。我们进一步提出了一个线性预处理器的通道输出的应用和优化。通过修改观测模型，预处理可以有效地改变底层因子图，从而显著提高检测性能，同时降低复杂度。
英文摘要：
We study the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Cyclic factor graphs have the potential to yield low-complexity symbol detectors, but are suboptimal if the ubiquitous sum-product algorithm is applied. In this paper, we present and evaluate strategies to improve the performance of cyclic factor graph-based symbol detection algorithms by means of neural enhancement. In particular, we apply neural belief propagation as an effective way to counteract the effect of cycles within the factor graph. We further propose the application and optimization of a linear preprocessor of the channel output. By modifying the observation model, the preprocessing can effectively change the underlying factor graph, thereby significantly improving the detection performance as well as reducing the complexity.
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