ISAC for Intelligent Transportation: Ray-Tracing, Clutter Cancellation, and Sensing-Aided Beamforming
Madhumitha Murthy, Hao Lin, Xiaojuan Zhang, Yonghong Zeng, Zhiping Lin, Yongming Chen, Francois Chin Po Shin, Sumei Sun · 2026
Integrated Sensing and Communication (ISAC) is rapidly gaining prominence as a foundational capability for intelligent transportation systems, allowing wireless networks to perform simultaneous data transmission and environmental sensing using shared resources. However, accurate performance evaluation of ISAC sys- tems remains challenging due to complex multipath propagation, high vehicular mobility, and rapidly time-varying channel conditions. This dissertation presents a unified simulation and algorithmic framework for ISAC in vehicular scenar- ios. A three-dimensional (3D) ray-tracing simulation platform is developed using the NVIDIA Sionna RT framework to model realistic wireless channels, includ- ing multipath effects, Doppler shifts, and environmental clutter. Based on this platform, an Orthogonal Frequency Division Multiplexing (OFDM)-based radar sensing algorithm incorporating Static Clutter Cancellation (SCC) is developed to enhance vehicle range and velocity estimation performance under strong ur- ban clutter conditions. Additionally, a sensing-based trajectory prediction algo- rithm is developed to support predictive beamforming through proactive beam alignment toward moving vehicles. Simulation results demonstrate measurable improvements in localization accuracy and communication reliability under high- mobility conditions compared to reactive, non-predictive beamforming schemes. The proposed framework provides a practical toolset for assessing and develop- ing advanced ISAC solutions tailored to next-generation intelligent transportation infrastructure.