Next-generation wireless networks promise to provide extremely high data rates, especially exploiting the so-called millimeter-wave frequency range. Gaining information from spectrum usage is becoming important to provide smart adaptation capabilities to future network protocol stacks. Issues such as deafness, misaligned antennas, or blockage may severely impact network performance, and their identification is crucial. Despite the complexity of full analytical models, machine learning techniques are progressively being considered to improve spectrum usage at higher layers. In this paper, we design a signal processing technique that uses narrowband physical layer energy traces, obtained from one or multiple channel sniffers. The proposed technique utilizes a combination of template matching and an Explicit Duration Hidden Markov Model (EDHMM) to correctly classify frames, while coping with the non-stationarity of the traces. This leads to a protocol level monitor that does not need to decode the channel at the physical layer, but just infers the type of packets that are exchanged based on sub-sampled energy traces. The performance of this framework is evaluated using off-the-shelf mm-wave wireless devices, quantifying its detection performance in the presence of one or multiple sniffers, and assessing the impact of physical layer parameters such as noise power and signal levels.

中文翻译：

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使用毫米波窄带能量轨迹的基于机器学习的网络分析

下一代无线网络有望提供极高的数据速率，尤其是利用所谓的毫米波频率范围。从频谱使用中获取信息对于为未来的网络协议栈提供智能适配功能变得越来越重要。耳聋、天线未对准或阻塞等问题可能会严重影响网络性能，因此识别它们至关重要。尽管完整分析模型很复杂，但人们逐渐考虑使用机器学习技术来改善更高层的频谱使用。在本文中，我们设计了一种信号处理技术，该技术使用从一个或多个通道嗅探器获得的窄带物理层能量轨迹。所提出的技术利用模板匹配和显式持续时间隐马尔可夫模型 (EDHMM) 的组合来正确分类帧，同时应对轨迹的非平稳性。这导致协议级监视器不需要在物理层解码信道，而只是根据子采样能量跟踪推断交换的数据包类型。该框架的性能使用现成的毫米波无线设备进行评估，在一个或多个嗅探器存在的情况下量化其检测性能，并评估物理层参数（如噪声功率和信号电平）的影响。但只是根据二次采样的能量轨迹推断交换的数据包类型。该框架的性能使用现成的毫米波无线设备进行评估，在一个或多个嗅探器存在的情况下量化其检测性能，并评估物理层参数（如噪声功率和信号电平）的影响。但只是根据二次采样的能量轨迹推断交换的数据包类型。该框架的性能使用现成的毫米波无线设备进行评估，在一个或多个嗅探器存在的情况下量化其检测性能，并评估物理层参数（如噪声功率和信号电平）的影响。