Search Results (6)

A single-band-notched ultra-wideband (UWB) low-sidelobe planar array antenna for millimeter-wave (mmWave) applications is presented. The antenna element employs a planar dipole excited through an H-shaped coupling slot to achieve broadband impedance matching, while a centrally loaded parasitic patch acts as a half-wavelength resonator to generate a controllable notch band. Additional parasitic patches are introduced to recover the high-frequency matching without degrading the notch response. An $8\times 8$ array is then developed using a Taylor-weighted feed network implemented with three classes of 1-to-4 microstrip power dividers. Measured results show that the array operates from 19.0 to 45.0 GHz with $\mathrm{VSWR}<2$, while providing a rejection band from 35.0 to 38.5 GHz. The notch suppresses the realized gain by about 5 dB around 37.0 GHz, the peak gain reaches 20.5 dBi in the passband, and average sidelobe levels better than $-17$ dB are obtained. The proposed design provides a practical approach for combining ultra-wide bandwidth, in-band interference rejection, and low-sidelobe radiation in a compact mmWave planar array. Full article

High-performance difference patterns (DPs) are critical for compact and integrated microwave array systems, particularly in monopulse tracking and beam-scanning applications. However, the design of monopulse phased arrays with steep slopes, high directivity, low sidelobes, and symmetric main lobes remains challenging due to constraints imposed by the array aperture and radome structure. In this paper, a novel design method is proposed to maximize the DP directivities for monopulse linear and planar phased arrays composed of microstrip patch antennas. The DP synthesis problem is first formulated as a nonconvex optimization model for directivity maximization. By fixing the reference phase of the DP slope and applying a first-order Taylor expansion of the quadratic function, the original problem is decomposed into a sequence of convex subproblems that can be solved efficiently. The proposed method fully exploits the flexibility of the phased array feed network, enabling directivity enhancement without altering the geometric configuration of the monopulse array. Finally, three numerical examples employing a radome-enclosed linear array, a uniform planar array, and a radome-enclosed planar array are presented to demonstrate the effectiveness of the proposed method in achieving the monopulse array DP synthesis with high directivity and symmetric main lobes. Full article

To address the critical demand for high-gain, low-sidelobe, and high-efficiency antennas in W-band arrays, this work presents a low-sidelobe all-metal array antenna based on ridge gap waveguide technology. The design employs a three-layer contactless metal structure, integrating a stepped-ridge feeding network with Taylor amplitude distribution and a higher-order mode resonant cavity. This integration enables efficient power distribution and low-loss transmission while eliminating the need for conventional welding or bonding processes. Measurement results indicate that the antenna exhibits a reflection coefficient below −10 dB across the 92.5–103.5 GHz. The in-band gain exceeds 25.8 dBi with less than 1 dB fluctuation, and the radiation efficiency surpasses 78%. Specifically, the sidelobe levels in both E- and H-planes remain below −17.5 dB, reaching under −19.5 dB at 94 GHz, while cross-polarization is better than −30 dB. The proposed antenna demonstrates high gain, low sidelobe, and high efficiency, showing promising potential for applications in millimeter-wave radar, imaging, and 6G communication systems. Full article

This paper deals with a family of normalized multivariate neural network (MNN) operators of complex-valued continuous functions for a multivariate context on a box of ${\mathbb{R}}^{\overline{N}}$, $\overline{N}\in \mathbb{N}$. Moreover, we consider the case of approximation employing iterated MNN operators. In addition, pointwise and uniform convergence results are obtained in Banach spaces thanks to the multivariate versions of trigonometric and hyperbolic-type Taylor formulae on the corresponding feed-forward neural networks (FNNs) based on one or more hidden layers. Full article

It is the tradition for the fluid community to study fluid dynamics problems via numerical simulations such as finite-element, finite-difference and finite-volume methods. These approaches use various mesh techniques to discretize a complicated geometry and eventually convert governing equations into finite-dimensional algebraic systems. To date, many attempts have been made by exploiting machine learning to solve flow problems. However, conventional data-driven machine learning algorithms require heavy inputs of large labeled data, which is computationally expensive for complex and multi-physics problems. In this paper, we proposed a data-free, physics-driven deep learning approach to solve various low-speed flow problems and demonstrated its robustness in generating reliable solutions. Instead of feeding neural networks large labeled data, we exploited the known physical laws and incorporated this physics into a neural network to relax the strict requirement of big data and improve prediction accuracy. The employed physics-informed neural networks (PINNs) provide a feasible and cheap alternative to approximate the solution of differential equations with specified initial and boundary conditions. Approximate solutions of physical equations can be obtained via the minimization of the customized objective function, which consists of residuals satisfying differential operators, the initial/boundary conditions as well as the mean-squared errors between predictions and target values. This new approach is data efficient and can greatly lower the computational cost for large and complex geometries. The capacity and generality of the proposed method have been assessed by solving various flow and transport problems, including the flow past cylinder, linear Poisson, heat conduction and the Taylor–Green vortex problem. Full article

The study and exploration of massive multiple-input multiple-output (MMIMO) and millimeter-wave wireless access technology has been spurred by a shortage of bandwidth in the wireless communication sector. Massive MIMO, which combines antennas at the transmitter and receiver, is a key enabler technology for next-generation networks to enable exceptional spectrum and energy efficiency with simple processing techniques. For massive MIMOs, the lower band microwave or millimeter-wave band and the antenna are impeccably combined with RF transceivers. As a result, the 5G wireless communication antenna differs from traditional antennas in many ways. A new concept of the MIMO tri-band hexagonal antenna array is being introduced for next-generation cellular networks. With a total scaling dimension of 150 × 75 mm², the structure consists of multiple hexagonal fractal antenna components at different corners of the patch. The radiating patch resonates at 2.55–2.75, 3.45–3.7, and 5.65–6.05 GHz (FR1 band) for better return loss (S11) of more than 15 dB in all three operating bands. The coplanar waveguide (CPW) feeding technique and defective ground structure in the ground plane have been employed for effective impedance matching. The deviation of the main lobe of the radiation pattern is achieved using a two-element microstrip Taylor antenna array with series feeding, which also boosts the antenna array’s bandwidth and minimizes sidelobe. The proposed antenna is designed, simulated, and tested in far-field radiating conditions and generates tri-band S-parameters with sufficient separation and high-quality double-polarized radiation. The fabrication and testing of MIMO antennas were completed, where the measurement results matched the simulation results. In addition, the 5G smartphone antenna system requires a new, lightweight phased microwave antenna (μ-wave) with wide bandwidth and a fire extender. Because of its decent performance and compact architectures, the proposed smartphone antenna array architecture is a better entrant for upcoming 5G cellular implementations. Full article