Co-Simulation framework for network-optimized ad hoc communication using an inverted AI edge simulation approach

Vehicular ad hoc networks (VANETs) demand seamless integration of mobility modeling, network optimization, and edge intelligence to meet the requirements of next-generation intelligent transportation systems. Existing studies often treat these domains separately, limiting realism and deployment readiness. This paper presents a co-simulation framework for network-optimized ad hoc communication using an inverted AI edge simulation approach. The framework in CARLA driving simulator with OMNeT++is to synchronize vehicle dynamics and network behavior, while a lightweight YOLOv8-Nano model deployed at the edge enables real-time object detection under constrained resources. Transformer-based architectures are incorporated for collaborative V2X perception, enhancing resilience against occlusion and dense traffic conditions. To ensure reliable connectivity, adaptive multi-network communication strategies are applied, integrating IEEE 802.11p, LTE- V2X, and 5G-V2X in compliance with ETSI and Third Generation Partnership Project (3GPP) standards. Experimental results demonstrate reductions in latency, improvements in detection accuracy, and higher packet delivery reliability compared to traditional approaches. The proposed framework establishes a scalable foundation for deploying edge-enhanced, AI-driven vehicular networks through realistic co- simulation.