Iet Microwaves Antennas & Propagation最新文献

Via-holes transition is an important component in multi-layer microwave and millimetre wave circuit systems, directly affecting signal transmission performance. In order to improve the millimetre wave performance of via-holes transition, the electromagnetic design automation software has been used to optimise the circuits design, which could consume a plenty of computer resources. In recent years, deep neural network (DNN) has been widely applied in the research of microwave component and is expected to solve this challenging and time-consuming problem. Employing large labelled datasets to obtain high-performance DNN model is desired but troublesome. Therefore, a transfer learning with deep neural network (TLDNN) surrogate model is proposed to improve the modelling efficiency. The experimental validation demonstrates that, compared with the conventional DNN, the TLDNN can reduce the amount of training data required without losing accuracy and accelerating modelling speed for behaviour prediction of via-holes transition. A prototype via-holes transition fabricated on multilayer liquid crystal polymer (LCP) substrate exhibits an average S₁₁ deviation of less than 2.9 dB between the measured and predicted results.

This paper proposed the design of an ultra-wideband antenna array with low radar cross section (RCS) based on a composite metasurface. In the high-frequency region, an ultra-wideband polarisation conversion metasurface (PCM) unit is designed. Under normal incident wave illumination, a pair of mirrored PCM units operate in anti-phase, effectively achieving radar cross section reduction (RCSR). In the lower frequency region, a corner-cut square patch is introduced, which generates an approximate $��180�°�$ phase shift relative to the PCM unit, enabling RCSR through phase cancellation. These corner-cutted square patches simultaneously serve as antenna radiators, thereby simplifying the design and reducing structural complexity. Simulation results demonstrate that the metasurface-excited antenna array operates within a frequency range of 10$��\sim �$11.5 GHz, achieving a peak gain of 12.5 dBi. The in-band and out-of-band RCSR bandwidth reaches 125.6%. Compared with the reference antenna and reference metasurfaces composed of a single type of unit, the proposed antenna not only broadens the RCSR bandwidth but also enhances the RCSR performance. Finally, the fabricated antenna is measured, and the measured results align well with the simulations, confirming the proposed design's effectiveness, antenna array, metasurface, radar cross section, ultra-wideband, and phase cancellation.

In this paper, a hybrid coding method for chipless radio-frequency identification (RFID) tag with angular orientation is proposed. Frequency-shift coding is achieved by controlling the size of three complementary split single ring resonators (CSSRR). And on the basis, we add a dimension of polarisation coding, which is achieved through changes in the radar cross-section (RCS) response caused by the rotation of CSSRR. Three angular-orientation structures are designed to identify the polarised rotating angle of each CSSRR by their changes in the RCS response. The simulated and measured results show that the hybrid frequency-polarisation coding can generate 884736 coded IDs (19.75 bits), which is 216 times more than the number of IDs generated by the frequency coding only.

In this work, a tuning element with controllable air gap integrated and implemented in a rectangular Ku-band WR62 waveguide is presented. The proposed tuning element concept, consists of two, top and bottom, thin conductive parallelepiped arms, which are placed in the middle of the rectangular waveguide structure. The bottom arm is bending to control the gap width (w) between the two arms, whereas the second arm is fixed. The curvature of the bending arm is controlled by a piezo-electric actuator, which affects the phase shift of the re-scattered E-field at the output port. The tuning element was designed with a 5% bandwidth centred at 15 GHz. The compact size, low-cost and the easy-to-manufacture proposed design offers a considerable phase shift with very low insertion loss, given its electrical size and operational waveband. A prototype, for the distinct frequency band, has been manufactured and measured. The same prototype has been simulated in ANSYS HFSS. The numerical results will be later used to validate the actual electromechanical prototype.

Manipulating near-field electromagnetic wavefronts is crucial for hyperthermia, compact test range systems and wireless power transmission. This paper investigates a design methodology of transmitarrays for near-field wavefront shaping. Unlike conventional methods that rely on trial and error or optimisation procedures, this approach establishes practical relationships to determine transmitarray specifications. At first, the achievable resolution is calculated based on the observation distance. Then, transmitarray specifications are calculated based on the number of observation points, observation distance and achievable resolution. This methodology enables a swift and precise design process by integrating the Moore–Penrose inverse technique and dyadic Green's functions (DGFs). Detailed examples demonstrate the effectiveness of this approach, achieving desired electromagnetic patterns while reducing complexities and design iterations. This work significantly advances the understanding and practical implementation of near-field shaping techniques, particularly for applications requiring tailored electromagnetic patterns. Analytical and experimental results rigorously validate the problem formulation, confirming the proposed algorithm's capability and highlighting its potential for widespread application.

A characteristic of the E-plane circulator is that it displays two neighbouring solutions for the first and second circulation conditions. The so-called small gap E-plane circulator was dealt with in the literature. This paper is concerned with the large gap geometry. It outlines a simple eigenvalue subroutine for the design of either arrangement, and includes some experimental data in WR75 waveguide for the degree-one large gap geometry. Good agreement was found between numerical simulations and experiments. High peak power analysis showed a significant improvement for large gap E-plane geometry, in comparison to the small gap E-plane as well as the H-plane junction circulator.

In this paper, a 1-THz graphene–metal-hybrid transmissive metasurface is proposed, which consists of six layers. By applying a dc biasing voltage, over a $��360�°�$-phase variation of the transmitted wave is obtained. Also a transmitarray for steering a pencil-beam in the 3D direction is designed using the proposed unit cell. Using the patch type structure in the transmitarray design has less surface wave and edge diffraction, in comparison with slot type transmitarray design, which leads to lower side lobe level and a wider steering angular rang. The obtained maximum gain is 23.5 dB. The designed beam steering is in $��\theta �=�0�°�-�45�°�$ and $��\varphi �=�0�°�-�360�°�$.

This paper proposes an anti-reconnaissance waveform and weight vector design method for frequency diverse array (FDA). According to the spatial spectrum estimation, the direction of arrival (DOA) mean square error (MSE) model is built based on the waveform and weight vector. The authors analyse the anti-reconnaissance capability of phased array (PA), Multiple-input Multiple-output (MIMO), and FDA. Moreover, the waveform and weight vector of the FDA can be designed to affect the estimation of the reconnaissance aircraft DOA. Then, reconnaissance aircraft DOA estimation MSE and radar DOA estimation MSE are combined to form an optimisation problem. The waveform and weight vector joint design method is proposed. The authors decompose the multivariate non-convex optimisation problem into waveform and weight vector optimisation subproblems. The theoretical analysis and simulation results show that the proposed method can affect reconnaissance aircraft estimation compared to other radars and methods. The radar DOA estimation performance can also be maintained well.

In recent years, there has been a significant surge in the utilisation of deep learning and machine learning techniques for addressing complex and time-intensive problems. The significance of employing deep learning becomes increasingly evident as the complexity of the problem increases. In the field of electromagnetics, the utilisation of deep learning techniques has exhibited exceptional efficacy across many applications, especially in wireless communications. In wireless communications, providing a structure that can simultaneously generate multiple beams and beamform them involves complexity and specific constraints. In this article, the time modulation technique is utilised to generate harmonics in the sidebands alongside the fundamental beam in various planar antenna arrays. By demonstrating the nonlinear shaping of the harmonic beams relative to each other, a novel approach is proposed that leverages deep learning techniques for the beamforming of both the fundamental beam and harmonic beams. In this regard, two models are proposed: a deep neural network (DNN) and a convolutional neural network (CNN). The input of CNN is comprised of two-dimensional patterns of the main beam and harmonics. The input to DNN, on the other hand, includes useful details about the main beam and harmonics, such as their scanning angles, side lobe levels and directivities. The output of the models consists of the time modulation parameters of the array elements, including the pulse width and the pulse delay. The results demonstrate that DNN has achieved better accuracy and a shorter processing time in comprehending the relationship between the time modulation of array elements with different array dimensions and the radiation pattern of the fundamental beam and harmonic beams. Additionally, several samples are presented to evaluate the proposed model. The results demonstrate a high level of accuracy in fundamental beamforming, as well as in harmonic beamforming and beam steering.

To address the time-consuming and computationally intensive challenges associated with antenna performance analysis using full-wave electromagnetic simulation software combined with global optimisation methods, this study proposes an efficient strategy based on deep learning, applied to high-precision antenna modelling. Considering the excellent performance of Convolutional Neural Networks (CNN) in pattern recognition and the high efficiency of Long Short-Term Memory (LSTM) structures of Recurrent Neural Networks (RNN) in handling sequential data, this paper combines CNN and LSTM structures to form a hybrid CNN-LSTM network. Furthermore, to enhance network performance and leverage the characteristic of CNNs in extracting image features within the receptive field by mimicking the biological visual cortex, the antenna to be modelled is constructed as a two-dimensional image. Thus, an Image-Model-CNN-LSTM hybrid network is proposed. This study employs two different antenna models to validate the generalisation capability of the proposed approach. Experimental results demonstrate that the proposed network exhibits significant advantages in terms of prediction accuracy and model fitting. Compared to the CNN-LSTM network, the proposed Image-Model-CNN-LSTM network applied to different antenna configurations achieves a reduction in Mean Squared Error (MSE) by 51.5% and 40.9%, respectively, while improving model fitting R² by 5.6% and 4.0%.