Vehicular Ad Hoc Networks (VANETs) play a pivotal role in the advancement of Intelligent Transportation Systems (ITS), facilitating real-time communication among vehicles (V2V) and between vehicles and infrastructure (V2I). However, maintaining reliable Quality of Service (QoS) in these dynamic environments remains challenging due to high mobility, frequent topology changes and interference. This paper proposes a robust cross-layer framework that integrates channel prediction and dynamic rate adaptation to address these challenges. The framework employs advanced multi-user detection techniques, including matched filters, successive interference cancellation (SIC), decorrelators and MMSE receivers, combined with adaptive multi-factor spreading, multi-code and multi-modulation transmission strategies. The study evaluates the framework’s performance through extensive simulations using a Software-Defined Radio (SDR) platform. Key findings demonstrate significant improvements in packet reception rate, throughput and spectral efficiency under various mobility and channel conditions. The proposed approach effectively mitigates interference and adapts to dynamic network environments, showcasing its potential to enhance reliability, scalability and efficiency in VANETs. Future work will explore real-world implementation and iterative algorithmic enhancements to further optimize QoS delivery in highly variable vehicular communication scenarios.
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