In this paper, an electronically reconfigurable folded reflectarray antenna (ERFRA), which includes 14×14 reflecting elements and features a fully planar compact configuration and wide-angle beam-scanning performance, was developed, fabricated, and tested. A novel reflecting element that is able to carry out cross-polarization rotation and electronically reconfigurable 1-bit compensation phases was first developed. After experimentally demonstrating its feasibility, the new reflecting element was then applied to the design of a 14×14 electronically reconfigurable folded reflectarray antenna, with a planar feed in the center of the reflectarray and a beam-control system based on the field programmable gate array (FPGA). The measurements of the fabricated ERFRA prototype show a wide beam-scanning range, up to ±60°, a peak gain of 17.07 dBi, and an aperture efficiency of about 13.2%.

We demonstrate a broadband near-100%-efficiency ultrathin metasurface operating at microwave and millimeter-wave frequencies. We develop and employ two orthogonally polarized metallic gratings to form a Fabry–Perot cavity and incorporate a subwavelength metallic double-split-ring resonator at the center of each unit cell. It allows arbitrary amplitude-phase combinations with no coupling between amplitude and phase or between transmitted and reflected waves, leading to the design of an ultrathin but highly efficient broadband metasurface with multiple functionalities. Furthermore, the proposed metasurface can generate diffractive beams with different orders and vortex beams with different orbital angular momentum (OAM) modes in reflection and transmission spaces simultaneously. Both numerical and experimental results verify that the proposed metasurface has superior performance to its counterparts that are based solely on phase control. The proposed metasurface presents a lightweight, low-cost, and easily deployable flat device for microwave and millimeter-wave applications.

Multibeam antennas have found more and more applications in current and future communication systems. In this letter, a low-profile multibeam fully planar folded transmitarray antenna (FTA) based on the bifocal principle is proposed. It consists of a reflectarray, a transmitarray, and a group of identical planar feeding antennas. The transmitarray is developed with the metasurface and achieves high transmission efficiency and 3 b compensation phases. A new technique is proposed to improve the isolations among the feeding antennas by taking advantage of the multiplicity of the feeding elements. As an example, a multibeam FTA operating at the Ku-band was designed, fabricated, and tested. Experimental results achieve a peak gain of 22.7 dBi and a scanning range of ±21°, with a maximum feed isolation of 24 dB.

In microwave wireless power receivers, impedance matching networks can maximize power transmission from RF to dc. However, they require circuit components and physical space to implement. To circumvent the problem, we present two compact patch rectennas that do not require matching networks. They comprise a patch antenna and rectifying Schottky diodes which are mounted on the ground plane and connected to the patch antenna through metallic vias. The positions of the vias are chosen in such a way that the input impedance of the antenna and that of the rectifying units are conjugately matched. The two rectenna types are all fabricated and tested: one with one rectifying unit and the other with double rectifying units, respectively. Measurements show that the rectenna with the single rectifying unit has the peak RF-dc conversion efficiency of 77% at 2.45 GHz and the dynamic power range (>60%) is 10 dB. The rectenna with the double rectifying units has the peak conversion efficiency of 74.14% at 2.45 GHz and the dynamic power range (>60%) is 10 dB. Both rectennas are good candidates for integrated microwave wireless power receivers due to the removal of the impedance matching networks and the suppression of harmonic components.

Deep learning has recently garnered significant interest in many applications especially for big data analytics in the edge computing environment. Federated learning, as a novel machine learning technique, aims to build a shared learning model from training data on distributed edge nodes to protect data privacy. However, the model update in federated learning requires parameter exchanges among edge nodes, which is rather bandwidth-consuming. This article proposes a novel distributed hierarchical tensor deep computation model by condensing the model parameters from a high-dimensional tensor space into a set of low-dimensional subspaces to reduce the bandwidth consumption and storage requirement for federated learning. Moreover, an updating approach with a hierarchical tensor back-propagation algorithm is developed by directly computing the gradients of low-dimensional parameters to reduce the memory requirement of training for edge nodes and improve training efficiency. Finally, extensive simulations on classical datasets with different local data distributions are presented for the performance evaluation. The results demonstrate that the proposed model relieves the burden of communication bandwidth and reduces energy consumption at edge nodes for federated learning.

This paper considers covert communications in the context of unmanned aerial vehicle (UAV) networks, where a UAV is employed as a base station to transmit covert data to a legitimate ground user, while ensuring that the data transmission cannot be detected by a warden. Aiming at maximizing the legitimate user’s average effective covert throughput (AECT), the UAV’s trajectory and transmit power are jointly optimized. Taking advantage of deep reinforcement learning (DRL) on solving dynamic and unpredictable problems, we develop a twin-delayed deep deterministic policy gradient aided covert transmission algorithm (TD3-CT), to determine the UAV’s optimal trajectory and transmit power. Furthermore, by introducing a reward shaping mechanism, the convergence of the algorithm is guaranteed. The experiment results show that the developed TD3-CT algorithm not only enables the covert transmission but also significantly improves its performance in termed of achieving a higher AECT, compared with the benchmark schemes.