  Orthogonal time frequency space (OTFS) modulation has emerged as a promising
solution to support high-mobility wireless communications, for which,
cost-effective data detectors are critical. Although graph neural network
(GNN)-based data detectors can achieve decent detection accuracy at reasonable
computational cost, they fail to best harness prior information of transmitted
data. To further minimize the data detection error of OTFS systems, this letter
develops an AMP-GNN-based detector, leveraging the approximate message passing
(AMP) algorithm to iteratively improve the symbol estimates of a GNN. Given the
inter-Doppler interference (IDI) symbols incur substantial computational
overhead to the constructed GNN, learning-based IDI approximation is
implemented to sustain low detection complexity. Simulation results demonstrate
a remarkable bit error rate (BER) performance achieved by the proposed
AMP-GNN-based detector compared to existing baselines. Meanwhile, the proposed
IDI approximation scheme avoids a large amount of computations with negligible
BER degradation.
