Processed frame-disjoint scenario data, source code, and result tables for Exact R-SAA in RSU-assisted vehicular networks

This reproducibility package supports the article “Joint Communication, Computing, and Sensing Resource Allocation with User-Level Tail-Latency Control in RSU-Assisted Vehicular Networks.” It contains processed frame-disjoint DAIR-V2X-C-derived scenario arrays, experiment configurations, Python source code, final result tables and arrays, audit files, learning-baseline checkpoints, and manuscript figures. The raw DAIR-V2X-C dataset is not included and must be obtained from its official repository. The package is intended to support verification and reproduction of the Exact R-SAA experiments, including the default reliability evaluation, equal-sensing-ratio comparisons, MM convergence, sensitivity analysis, user-scale scalability, and online timing results.