Ad Hoc网络无中心、自组织、动态拓扑且支持多路径通信,适合无人机组网。传统单信道MAC协议难以解决Ad Hoc网络中的隐藏终端和暴露终端问题,影响数据传输。本文探讨多信道MAC协议原理及其优劣性,通过OPNET平台对多信道MMAC协议和单信道IEEE 802.11DCF协议进行仿真。实验显示,MMAC协议在解决隐藏终端和暴露终端问题上表现卓越,且在吞吐量和丢包率等网络性能指标上优于单信道IEEE802.11协议。
低功耗广域网(Low Power Wide Area Network,LPWAN)技术的出现,能够在保证更远距离的通信传输的同时,最大限度地降低功耗,节约传输成本。LoRa(Long Range)技术作为其中的佼佼者,凭借其远距离、低功耗、大容量、强抗干扰、高接收灵敏度的特点,备受工业界和学术界的青睐。针对目前工业中主流使用的基于ALOHA的LoRaWAN协议无法很好地解决海量终端设备接入LoRa网络后所带来的严重数据包冲突以及LoRa CAD(Channel Activity Detection)功能带来的隐藏终端问题,提出了一种基于BTMA(Busy Tone Multiple Access)的LoRa网络MAC协议——BT-MAC协议。该协议利用了LoRa高接收灵敏度的特性,网关利用“忙音”信标来通知各个节点网关的工作情况,减少了无效包的发送。同时,节点端通过记录有“忙音”信息和本地信息的逻辑信道矩阵,结合最优信道选择算法,选出最优逻辑信道进行发送,降低了端节点上行数据包之间的冲突,有效缓解了LoRa网络中的隐藏终端问题以及阻塞问题。此外,搭建了LoRa网络MAC协议测试平台,并测试了BT-MAC的有效性,完成了室内和室外环境大规模的并发实验和能耗检测实验。实验结果表明,BT-MAC协议的吞吐量是LMAC-2协议的1.6倍,是ALOHA协议的5.1倍;同时其包接收率达到LMAC-2协议的1.53倍,ALOHA协议的17.2倍;其包接收平均能耗约为LMAC-2协议的64.1%,为ALOHA协议的14.2%。
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.
