  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.
