%0 Journal Article %T Channel Prediction for MAC Optimization in VANET, FANET Software Defined Radio Platform %A Pegdwindé %A Justin Kouraogo %A Hamidou Harouna Omar %A Dé %A siré %A Guel %J Engineering %P 124-135 %@ 1947-394X %D 2025 %I Scientific Research Publishing %R 10.4236/eng.2025.171008 %X This work addresses the critical challenge of ensuring reliable communication in vehicular ad hoc networks (VANETs) and drone networks (FANETs) under dynamic and high-mobility conditions. Current methods often fail to adequately predict rapid channel variations, leading to increased packet loss and degraded Quality of Service (QoS). To bridge this gap, we propose a novel cross-layer framework that integrates physical channel prediction into the Medium Access Control (MAC) layer to optimize network performance. Our framework employs an ARIMA (1, 0, 1) model for real-time channel prediction and dynamically adjusts MAC layer parameters to enhance throughput and reliability. Simulations demonstrate a 25% improvement in useful throughput and a 30% reduction in packet loss rates compared to baseline methods. These improvements enable practical applications in intelligent transportation systems and the efficient management of autonomous drones. Key contributions include: 1) Development of a cross-layer framework that integrates channel prediction and MAC optimization. 2) Demonstration of the framework’s effectiveness through Monte Carlo simulations in high-mobility scenarios. 3) Quantitative validation of enhanced throughput and reliability, highlighting the system’s potential for real-world deployment. %K Prediction %K Cross-Layer %K Multiuser Detection %K Packet Error Rate %K Goodput %U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=140263