Adaptive DPIM-based integrated sensing and communication signal generation for LiDAR in intelligent transportation systems

Abstract

LiDAR-integrated sensing and communication (ISAC) in intelligent transportation systems faces significant degradation from atmospheric turbulence and spatially varying channels. This paper proposes an adaptive DPIM-based signal generation method that adjusts modulation parameters per spatial sector. The method extracts scintillation indices and coherence times from LiDAR echo signals at different pointing angles. These features are fused into a turbulence intensity metric and partitioned by K-means clustering into three channel state levels. For each sector, the modulation order is selected via a BER constraint, and the time-slot width is set by range resolution. Each DPIM pulse is converted into an NLFM sweep pulse with channel-adaptive nonlinearity, enhancing sensing without affecting demodulation. Simulations show the method achieves a peak instantaneous rate of 2.4 Gbps and BER < 10⁻⁹ in good channels, and reduces to order 4 with BER < 10⁻³ in poor channels. The 2 GHz NLFM pulse improves range resolution from 0.15 m to 0.075 m. Compared to fixed schemes, it improves average rate by 2.3× and reduces BER by 92%. This provides a practical solution for high-reliability LiDAR ISAC in complex vehicular environments.