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 computation 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.

翻译：正交时频空间（OTFS）调制已成为支持高移动性无线通信的一种有前景的解决方案，而低成本的数据检测器对此至关重要。尽管基于图神经网络（GNN）的数据检测器能够在合理的计算成本下实现良好的检测精度，但它们未能充分利用传输数据的先验信息。为进一步降低OTFS系统的数据检测误差，本文开发了一种基于AMP-GNN的检测器，利用近似消息传递（AMP）算法迭代改进GNN的符号估计。考虑到多普勒间干扰（IDI）符号会给所构建的GNN带来显著的计算开销，本文实现了基于学习的IDI近似方法以维持较低的检测复杂度。仿真结果表明，与现有基线相比，所提出的AMP-GNN检测器在误码率（BER）性能上表现卓越。同时，所提出的IDI近似方案在BER性能几乎无损失的情况下，避免了大量计算开销。