Abstract

In vehicular networks, the propagation environment changes rapidly for mobile nodes. To achieve high throughput, wireless devices need to be highly adaptive to these environmental changes by altering their transmission parameters across different layers of the network stack. Sensors in mobile and vehicular nodes can be used to form an understanding of the surrounding context. Such contextual awareness is particularly important in vehicular networks as the frequent context switching and increased channel fluctuations can cause existing adaptation protocols to fail to converge to the optimal transmission parameters. In this paper, we leverage information about the environmental context to enable improved rate adaptation performance in vehicular networks. In particular, we propose a classification-based link-level adaptation framework, which can effectively learn the relationship between context information (such as velocity, SNR, and channel type) and the throughput of various transmission modes. We then quantify the throughput improvement using the proposed scheme and show that our proposed framework can significantly enhance the performance of rate adaptation. With experiments on emulated and in-field channels, we observe that the throughput increases by up to 245% over protocols which use SNR alone to make rate decisions. Based on an analysis of attribute importance, we identify channel type as a key parameter that affects classification performance. Since channel type often cannot be directly obtained, we propose a multi-dimensional channel inference method for use when knowledge about the channel type is not available. We demonstrate that the proposed channel inference achieves an accuracy of up to 94% in previously encountered channels and can quickly signal that a channel has not yet been encountered. The robustness of the proposed methods are demonstrated using experimental data from two different hardware platforms and three different carrier frequency bands. Lastly, we evaluate the most predominant Linux-based rate selection algorithm (Minstrel), study the relative rate selection accuracy of our approach, and analyze the key role that the retry mechanism in Minstrel plays on its performance.

中文翻译：

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车载网络中情境感知适应的体系结构和实验评估

摘要

在车载网络中，移动节点的传播环境变化迅速。为了实现高吞吐量，无线设备需要通过更改跨网络堆栈不同层的传输参数来高度适应这些环境变化。移动和车辆节点中的传感器可用于形成对周围环境的理解。这种上下文感知在车辆网络中尤其重要，因为频繁的上下文切换和增加的信道波动会导致现有的适配协议无法收敛到最佳传输参数。在本文中，我们利用有关环境环境的信息来提高车辆网络中的速率自适应性能。特别是，我们提出了一个基于分类的链接级适应框架，可以有效了解上下文信息（例如速度，SNR和信道类型）与各种传输模式的吞吐量之间的关系。然后，我们使用提出的方案量化吞吐量的提高，并表明我们提出的框架可以显着提高速率自适应的性能。通过在仿真和现场信道上进行的实验，我们观察到，与仅使用SNR进行速率决策的协议相比，吞吐量最多可提高245％。基于对属性重要性的分析，我们将渠道类型确定为影响分类性能的关键参数。由于通常无法直接获得通道类型，因此，我们提出了一种多维通道推断方法，供无法使用有关通道类型的知识时使用。我们证明了所提出的信道推断在先前遇到的信道中实现了高达94％的精度，并且可以快速发出尚未遇到信道的信号。利用来自两个不同硬件平台和三个不同载波频带的实验数据证明了所提出方法的鲁棒性。最后，我们评估了最主要的基于Linux的速率选择算法（Minstrel），研究了我们方法的相对速率选择精度，并分析了Minstrel中的重试机制对其性能起着关键作用。利用来自两个不同硬件平台和三个不同载波频带的实验数据证明了所提出方法的鲁棒性。最后，我们评估了最主要的基于Linux的速率选择算法（Minstrel），研究了我们方法的相对速率选择精度，并分析了Minstrel中的重试机制对其性能起着关键作用。利用来自两个不同硬件平台和三个不同载波频带的实验数据证明了所提出方法的鲁棒性。最后，我们评估了最主要的基于Linux的速率选择算法（Minstrel），研究了我们方法的相对速率选择精度，并分析了Minstrel中的重试机制对其性能起着关键作用。