高速列车无线通信系统隧道场景通道测量与模型综述

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本文是一篇关于高速列车无线通信系统在隧道场景中通道测量与模型的研究论文。随着高速列车(HST)的快速发展,其无线通信系统面临新的挑战。文章着重讨论了隧道环境下的无线通信信道特性,因为隧道自身的长度限制、边界效应以及波导效应,使得隧道场景中的信道特性与其他HST场景显著不同。 在高速列车无线通信系统中,准确的信道模型对于设计和评估通信系统的性能至关重要。隧道环境下的信道模型需要同时考虑大尺度衰落和小尺度衰落特征。大尺度衰落通常涉及到路径损耗、多径传播和阴影衰落,而小尺度衰落则包括多普勒频移、相位噪声和快速衰落等现象。这些因素都会对无线信号的传输质量产生重大影响,尤其是在高速移动的列车与隧道壁相互作用时。 文章回顾了现有的隧道信道测量方法,这些方法通常包括实地测试、模拟实验以及基于已有数据的分析。实地测试通过车载或地面设备收集实际的无线信号数据,以便更准确地理解信号在隧道中的传播行为。模拟实验则利用计算机仿真技术来模拟不同的环境条件,如列车速度、隧道几何结构和无线频率。数据分析则通过对大量实测数据的处理,提取出信道的关键参数和统计特性。 作者还探讨了现有的信道模型,如几何射线追踪模型、统计模型和混合模型。几何射线追踪模型基于物理光学原理,能够精确描述信号的反射、折射和散射,但计算复杂度较高。统计模型则通过概率分布函数描述信道的统计特性,适用于快速信道估计和预编码。混合模型结合了这两种方法,试图在精度和计算效率之间找到平衡。 此外,论文还讨论了高速列车在隧道内通信面临的主要问题,如多普勒效应加剧、信号衰减加大以及干扰增强等。针对这些问题,研究者们提出了多种解决方案,如采用更宽的频谱资源、优化天线配置、利用多输入多输出(MIMO)技术和先进的信号处理算法。 这篇综述论文提供了高速列车无线通信系统在隧道环境中信道特性的全面概述,为未来的研究和系统设计提供了有价值的参考。它强调了在隧道环境下建立精确信道模型的重要性,并对解决相关技术挑战提出了可能的方向。

Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

2023-02-18 上传