Channel Prediction for MAC Optimization in VANET, FANET Software Defined Radio Platform
Channel Prediction for MAC Optimization in VANET, FANET Software Defined Radio Platform作者机构：Department of Computer Science Joseph Ki-Zerbo University Ouagadougou Burkina Faso Doctoral School of Science and Technology Aube Nouvelle University Ouagadougou Burkina Faso
出 版 物：《Engineering(科研)》 (工程(英文)(1947-3931))
年 卷 期：2025年第17卷第1期
页 面：124-135页
学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学]
主 题：Prediction Cross-Layer Multiuser Detection Packet Error Rate Goodput
摘 要：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.
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