Sparse Graph Attention Network Based Signal Detection for OTFS System

Orthogonal time-frequency space (OTFS) modulation has emerged as a promising solution for reliable communication in high-mobility scenarios, addressing the limitations of traditional orthogonal frequency-division multiplexing (OFDM) systems. However, existing OTFS detection methods, including linear, nonlinear, and AI-based detectors, struggle with either high computational complexity or suboptimal performance. To overcome these limitations, we propose a low-complexity graph attention network-based OTFS detector (GAT-OTFS) tailored for the reduced cyclic prefix (RCP) OTFS scenario. Our approach leverages the sparsity of the DD domain equivalent channel to reduce complexity by precisely constructing a sparse graph for GNN-based detection. By introducing an attention mechanism, the GAT-OTFS effectively captures the correlation between different channel paths, enhancing detection accuracy. Simulation results demonstrate that the GAT-OTFS offers substantial performance improvements over existing detectors, with reduced complexity, making it a viable solution for future high-mobility communication systems.