With the evolution of wireless sensing technology, WiFi-based human identification has demonstrated tremendous potential in human-computer interaction and home security. However, most existing research operates in controlled environments, overlooking the complexities of real-world scenarios, such as signal fading, signal interference and uncertainty, and the diversity of application requirements. This article presents a comprehensive analysis of a series of representative research articles on WiFi-based human identification and summarizes five major challenges in achieving high-precision identification in complex application scenarios. Non-line-of-sight (NLOS) user sensing, coexistence user sensing, dynamic group user sensing, cross-domain user sensing, and multi-task sensing. Additionally, this article proposes a series of current solutions, including improving the signal-to-noise ratio (SNR) of NLOS sensing from the perspective of communication signals and sensing models, addressing coexistence sensing issues, designing adaptable tasks for dynamic user groups, leveraging techniques like adversarial learning and transfer learning for cross-domain problems, and employing modular deep learning architectures. By providing a comprehensive overview of WiFi-based human identification, this survey not only offers insights into current research but also charts a roadmap for future investigations. It is anticipated that this survey will stimulate innovative research endeavors and foster the expansion of wireless sensing technology across diverse application domains.

As one of the most critical elements in the hydrological cycle, real-time and accurate rainfall measurement is of great significance to flood and drought disaster risk assessment and early warning. Using commercial microwave links (CMLs) to conduct rainfall measure is a promising solution due to the advantages of high spatial resolution, low implementation cost, near-surface measurement, and so on. However, because of the temporal and spatial dynamics of rainfall and the atmospheric influence, it is necessary to go through complicated signal processing steps from signal attenuation analysis of a CML to rainfall map. This article first introduces the basic principle and the revolution of CML-based rainfall measurement. Then, the article illustrates different steps of signal process in CML-based rainfall measurement, reviewing the state of the art solutions in each step. In addition, uncertainties and errors involved in each step of signal process as well as their impacts on the accuracy of rainfall measurement are analyzed. Moreover, the article also discusses how machine learning technologies facilitate CML-based rainfall measurement. Additionally, the applications of CML in monitoring phenomena other than rain and the hydrological simulation are summarized. Finally, the challenges and future directions are discussed.

The explosive development of emerging telecommunication services has stimulated a huge growth in bandwidth demand as people seek universal access to telecommunication networks. In addition, the kinds of services of an existing optical access network are becoming more flexible. In order to provide higher capacity and meet higher transmission performance requirements, it is necessary to further explore the application of the beyond-100G passive optical network (PON). This paper offers a comprehensive review and outline of the prospects of technologies for bringing a beyond-100G PON to practical applications in the future. We review the current existing technologies, mainly in terms of the physical layer and higher media access control layer. These key technologies for the beyond-100G PON, which plays an increasingly significant role, include the advanced multiplexing technology, physical layer digital signal processing technology, infrastructure-sharing technology, security protection technology, and intelligent control management key technologies. Finally, open issues and new challenges for the next-generation PON are focused upon.

Commercial microwave links (CMLs) are emerging as effective tools for rainfall monitoring. However, the performance of CML‐based rainfall monitoring is influenced by various uncertainties. This paper aims to assess the impact of these uncertainties. Initially, the paper outlines the theoretical basis and methodology for deriving rainfall from the original received signal level (RSL) data of a CML. From a novel perspective, uncertainties are classified into four aspects: experimental system, environmental and meteorological factors, data preprocessing methods, and models at each step. Then, utilizing RSL data from three experimental CMLs installed in Jiangyin, China, the study evaluates the impacts of path length, atmospheric conditions, wet antenna, different data preprocessing methods, and model parameters. Moreover, an open‐source dataset is employed to analyze the effects of sampling rate. The results reveal that RSL decreases with increasing path length and exhibits a diurnal pattern. Wet antenna attenuation (WAA) is identified as a critical factor contributing to uncertainties in CML‐based rainfall monitoring, with its influence becoming more pronounced as the path length decreases and rainfall intensity increases. A higher sampling rate is recommended for wet/dry classification. Filtering preprocessing of RSL data has less influence on short‐time Fourier transform (STFT)‐based rain rate estimation but affects algorithms that directly use RSL data for baseline determination. Additionally, variations in model parameters introduce uncertainties, necessitating careful calibration.