A machine learning-assisted quasi-bisection method (MLAQBM) is proposed for the broadband optimization of the pixelated patch antennas. Distinct from traditional MLA optimization methods, MLAQBM employs a coarse-to-fine pixel partition and machine learning optimization to iteratively refine the shape of the antenna. Two implementation strategies are introduced. In the first strategy, larger pixels are initially partitioned and samples are collected. A surrogate model is then established using convolutional neural networks (CNN), and an initial shape is derived through particle swarm optimization (PSO). Again, further refinement is achieved by finer pixel partitioning and subsequent CNN-based PSO. The second strategy leverages a pre-existing broadband E-shaped patch design, which is then subdivided into smaller pixels. By collecting samples, training a CNN surrogate model, and performing PSO optimization, a broader bandwidth is realized. Compared with the reported MLA optimization methods, MLAQBM establishes a stable surrogate model that is robust, easy to converge, and adaptable to different optimization objectives. To validate the proposed strategies, the optimized pixelated patch antennas are fabricated and measured. The measured results demonstrate that the bandwidth of the proposed patch antennas achieves 24.5% and 32.5%.

We designed a kind of new vortex beam generator based on a planar all-dielectric metamaterial in the mid-infrared band. The height of this generator remains constant in the plane, and the effective refractive index increases gradually in the azimuthal direction which depends on subwavelength aperture columns with gradual diameters in the dielectric flat plate. Two types of vortex beam generators including transmissive- and reflective-type generators are designed where the thickness of the latter is half of the former. Simulation results show that both vortex beam generators successfully produce mid-infrared vortex beams with a topological charge number of one. This planar vortex beam generator based on a dielectric metamaterial has the advantages of simple structure, easy processing and low optical absorption.

The classic trilateration technique can localize each target based on its distances to three anchors with known coordinates. Usually, this technique requires all the anchors and targets, e.g., the satellites and the mobile phones in Global Navigation Satellite System (GNSS), to actively transmit/receive radio signals such that the delay of the one-way radio signal propagated between each anchor and each target can be measured. Excitingly, this paper will show that the trilateration technique can be generalized to the scenario where one of the three anchors and all the targets merely reflect the radio signals passively as in radar networks, even if the propagation delay between the passive IRS and the passive targets is difficult to be measured directly, and the data association issue for multi-sensor multi-target tracking arises. Specifically, we consider device-free sensing in a cellular network consisting of two base stations (BSs), one passive intelligent reflecting surface (IRS), and multiple passive targets, to realize integrated sensing and communication (ISAC). The two BSs transmit the orthogonal frequency division multiplexing (OFDM) signals in the downlink and estimate the locations of the targets based on their reflected signals via/not via the IRS. We propose an efficient trilateration-based strategy that can first estimate the distances of each target to the two BSs and the IRS and then localize the targets. Numerical results show that the considered networked sensing architecture with heterogenous anchors can outperform its counterpart with three BSs.

A machine-learning online optimization (MLOO) method is proposed for the bandwidth optimization of a quasi-Yagi antenna. The initial structural parameters are derived from scaling theory, providing a physically informed starting point for subsequent optimization. In the MLOO, the adaptive dynamic adjustment of the parameter search range in a small-sample scenario is utilized to accelerate convergence. Specifically, a limited set of samples is generated via small-scale expansion to form the initial training set, upon which a Gaussian process regression (GPR) surrogate model is constructed. Sobol sensitivity analysis is combined with boundary proximity detection to refine the search space. The low sensitivity range is slightly reduced, and the high sensitivity range is reasonably expanded. Within this dynamic search range, a genetic algorithm efficiently explores candidate solutions while the surrogate model is updated iteratively. To validate the proposed MLOO method, two reported quasi-Yagi antennas are employed and optimized. The experimental results indicate that only 72 and 127 full-wave simulations are required. The measured reflection coefficients remain below -10 dB from 9 GHz ∼11.5 GHz. Compared to traditional MLOO methods, the optimization time is significantly reduced, thereby substantially improving design efficiency.

In this paper, a broadband photoconductive antenna (PCA) with enhanced full-band radiation power is proposed based on dual-frequency complementary technology. In the proposed PCA, dual-frequency metallic bar resonators are combined with the coplanar transmission line. Dual-frequency resonant cascades in the meta-atomic electrodes enable effective manipulation of the dissipated terahertz energy along the coplanar lines of PCAs and efficient scattering of terahertz energy into the far field, thereby enhancing far-field radiation power. To validate the proposed antenna, the prototype of the proposed PCA is manufactured and measured. Compared with the conventional PCA, experimental results indicate that our PCA increases the THz radiation power of the entire radiation frequency band (0.02–1.5 THz) by 4.5 times. In addition, our experiments demonstrate that the proposed PCA overcomes the narrowband resonant response characteristics of traditional methods, significantly improving energy utilization efficiency. This design offers a reproducible and universal approach to effectively harness this dissipated terahertz energy, opening a path to rapidly advancing the practicality of terahertz techniques.