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Capacitive proximity sensors (CPSs) are ubiquitous because of their simple design, low cost and low consumption. Capacitive displacement sensing, as one of the three sensing modalities, works for long distance and can be unitized to measure more physical quantities compared with capacitive volume and deformation sensing. In this paper, we firstly introduce the concept of capacitive displacement sensing. After that, we present applications of capacitive displacement sensing under three broad categories: distance measurements, indirect measurements, and the applications applied in smart environments. Finally, we discuss the challenges and possible solutions for CPSs development. We show that both the detection range and accuracy of CPS can be improved by multi-sensor fusion, and the application scenarios can be extensive through machine/deep learning approaches. We aim to provide a comprehensive, and state-of-theart review of the capacitive displacement sensing, and inspire more researchers and developers to find wide application perspectives.INDEX TERMS Capacitive proximity sensor (CPS), capacitive displacement sensing, distance measurement, indirect measurement, smart environment.

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Wireless powered communication networks (WPC-Ns) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this paper, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices (IoTDs) by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station (IRBS), and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent (BCD) method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation (SCA) algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

To boost the performance of wireless communication networks, unmanned aerial vehicles (UAVs) aided communications have drawn dramatically attention due to their flexibility in establishing the line of sight (LoS) communications. However, with the blockage in the complex urban environment, and due to the movement of UAVs and mobile users, the directional paths can be occasionally blocked by trees and high-rise buildings. Intelligent reflection surfaces (IRSs) that can reflect signals to generate virtual LoS paths are capable of providing stable communications and serving wider coverage. This is the first paper that exploits a three-dimensional geometry dynamic channel model in IRS-assisted UAV-enabled communication system. Moreover, we develop a novel deep learning based channel tracking algorithm consisting of two modules: channel preestimation and channel tracking. A deep neural network with off-line training is designed for denoising in the pre-estimation module. Moreover, for channel tracking, a stacked bi-directional long short term memory (Stacked Bi-LSTM) is developed based on a framework that can trace back historical time sequence together with bidirectional structure over multiple stacked layers. Simulations have shown that the proposed channel tracking algorithm requires fewer epochs to convergence compared to benchmark algorithms. It also demonstrates that the proposed algorithm is superior to different benchmarks with small pilot overheads and comparable computation complexity.

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