Channel Prediction for MAC Optimization in VANET, FANET Software Defined Radio Platform  

作　　者：Pegdwindé Justin Kouraogo Hamidou Harouna Omar Désiré Guel Pegdwindé Justin Kouraogo;Hamidou Harouna Omar;Désiré Guel(Department of Computer Science, Joseph Ki-Zerbo University, Ouagadougou, Burkina Faso;Doctoral School of Science and Technology, Aube Nouvelle University, Ouagadougou, Burkina Faso)

机构地区：[1]Department of Computer Science, Joseph Ki-Zerbo University, Ouagadougou, Burkina Faso [2]Doctoral School of Science and Technology, Aube Nouvelle University, Ouagadougou, Burkina Faso

出　　处：《Engineering(科研)》2025年第1期124-135,共12页工程(英文)(1947-3931)

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.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.

关 键 词：Prediction CROSS-LAYER Multiuser Detection Packet Error Rate GOODPUT 

分 类 号：TN9[电子电信—信息与通信工程]