  Inter-carrier interference (ICI) caused by mobile reflectors significantly
degrades the conventional orthogonal frequency division multiplexing (OFDM)
performance in high-mobility environments. The orthogonal time frequency space
(OTFS) modulation system effectively represents ICI in the delay-Doppler
domain, thus significantly outperforming OFDM. Existing iterative and neural
network (NN) based OTFS detectors suffer from high complex matrix operations
and performance degradation in untrained environments, where the real wireless
channel does not match the one used in the training, which often happens in
real wireless networks. In this paper, we propose to embed the prior knowledge
of interference extracted from the estimated channel state information (CSI) as
a directed graph into a decoder untrained neural network (DUNN), namely
graph-based DUNN (GDUNN). We then combine it with Bayesian parallel
interference cancellation (BPIC) for OTFS symbol detection, resulting in
GDUNN-BPIC. Simulation results show that the proposed GDUNN-BPIC outperforms
state-of-the-art OTFS detectors under imperfect CSI.
