In recent years, the rapid development of urbanization has posed enormous challenges to transportation, security management, quality of life, and so on, which makes the research and development of smart city important. As an information-driven project, strong communication infrastructures are required for connecting smart objects, people, and sensors together. As a consequence, the optimization of the wireless network is the primary premise to support and improve the quality of smart services. The challenge lies in the difficulty to achieve the quantitative description of networks due to the complexity and variability of wireless environments. It is too coarse-grained to express characteristics of networks simply by the strength of received signals through mobile devices. To extract more fine-grained network characteristics, we dig into the PHY layer collecting CSI for network descriptions and extract three signal characteristics, i.e., Rician-K, delay spread and spectral width, from real-world wireless channels. The K-means clustering algorithm is implemented in this article for good performance of network partitioning, and experiments are conducted based on the data sets collected from real scenarios. Simulation results verify the feasibility of our scheme.

中文翻译：

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通过智能城市的物理层信息进行无线网络优化

近年来，城市化的迅猛发展对交通，安全管理，生活质量等提出了巨大的挑战，这使得智慧城市的研究与开发显得尤为重要。作为一个信息驱动的项目，需要强大的通信基础结构才能将智能对象，人和传感器连接在一起。因此，无线网络的优化是支持和改善智能服务质量的主要前提。挑战在于，由于无线环境的复杂性和可变性，难以对网络进行定量描述。仅通过移动设备接收到的信号的强度，就无法粗略地表达网络的特性。要提取更细粒度的网络特征，我们深入PHY层，收集用于网络描述的CSI，并从实际无线信道中提取三个信号特征，即Rician-K，延迟扩展和频谱宽度。本文实现了K-means聚类算法，以实现良好的网络分区性能，并基于从真实场景中收集的数据集进行了实验。仿真结果验证了该方案的可行性。