Contention-based learning MAC protocol for broadcast vehicle-to-vehicle communication
Contention-based learning MAC protocol for broadcast vehicle-to-vehicle communication
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DOI：
10.1109/vnc.2017.8275614
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Andreas Pressas;Zhengguo Sheng;F. Ali;Daxin Tian;M. Nekovee
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文献类型：
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作者：
Andreas Pressas;Zhengguo Sheng;F. Ali;Daxin Tian;M. Nekovee
车对车通信 (V2V) 是一项即将推出的技术，可以通过移动汽车之间的无线连接实现更安全、更高效的运输。指定 V2V 堆栈的物理层和媒体访问控制 (MAC) 层的关键支持技术是 IEEE 802.11p，它属于最初设计用于 WLAN 的 IEEE 802.11 协议系列。 V2V 网络是由车载站临时形成的，这些车载站依赖广播传输来提供其预期的服务和应用。由于 MAC 协议无法适应增加的网络流量，并且永远不会检测或恢复冲突数据包，因此广播本质上比单播对通道争用更加敏感。本文解决了 IEEE 802.11p MAC 协议固有的可扩展性问题。网络的密度范围可以从非常稀疏到数百个站点争相访问该信道。即使在城市网络中常见的密集拓扑中，合适的 MAC 也需要提供 V2V 交换的容量。我们提出了基于强化学习（RL）的 IEEE 802.11p MAC 的修改版本，旨在减少数据包冲突概率和带宽浪费。提供了有关学习算法调整和网络方面的实现细节。我们还提供了有关已实现的消息包传递和该解决方案可能的延迟开销的模拟结果。随着网络流量的增加，我们的解决方案与标准 IEEE 802.11p 相比，吞吐量提高了 70%，同时将传输延迟保持在可接受的水平内。
Vehicle-to-Vehicle Communication (V2V) is an upcoming technology that can enable safer, more efficient transportation via wireless connectivity among moving cars. The key enabling technology, specifying the physical and medium access control (MAC) layers of the V2V stack is IEEE 802.11p, which belongs in the IEEE 802.11 family of protocols originally designed for use in WLANs. V2V networks are formed on an ad hoc basis from vehicular stations that rely on the delivery of broadcast transmissions for their envisioned services and applications. Broadcast is inherently more sensitive to channel contention than unicast due to the MAC protocol's inability to adapt to increased network traffic and colliding packets never being detected or recovered. This paper addresses this inherent scalability problem of the IEEE 802.11p MAC protocol. The density of the network can range from being very sparse to hundreds of stations contenting for access to the channel. A suitable MAC needs to offer the capacity for V2V exchanges even in such dense topologies which will be common in urban networks. We present a modified version of the IEEE 802.11p MAC based on Reinforcement Learning (RL), aiming to reduce the packet collision probability and bandwidth wastage. Implementation details regarding both the learning algorithm tuning and the networking side are provided. We also present simulation results regarding achieved message packet delivery and possible delay overhead of this solution. Our solution shows up to 70% increase in throughput compared to the standard IEEE 802.11p as the network traffic increases, while maintaining the transmission latency within the acceptable levels.