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.

Accurate quality-of-transmission (QoT) estimation prior to lightpath deployment is essential for minimizing design margins in optical networks. Owing to their high precision and strong generalization capabilities, artificial neural networks (ANNs) have emerged as a promising approach for lightpath QoT estimation. However, focusing exclusively on prediction accuracy is inadequate for maximizing global network capacity. Conventional models employing symmetric loss functions apply identical penalties to both overestimation and underestimation errors, thereby precluding controlled bias in predictions and their impact on overall network capacity. This paper investigates the margin configuration for the whole network capacity and proposes a novel QoT estimation method with asymmetric loss functions, which jointly considers the assessment of global network capacity and gives different penalties for overestimation and underestimation. We further present an iterative search algorithm grounded in network capacity considerations to optimize the parameters of these asymmetric loss functions. Simulation results confirm that our ANN-based models facilitate efficient modulation format assignment, leading to corresponding increases in network capacity.