Iet Microwaves Antennas & Propagation最新文献

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%.

The paper introduces an effective method for synthesising large wideband rotationally symmetric sparse circular arrays. Initially, the approach involves incorporating several concentric auxiliary rings within the circular aperture comprised of rotationally symmetrical folds. The array elements are then distributed solely over these rings, thereby avoiding ineffective optimisation within each fold. Furthermore, an initial spacing is established and extended along the concentric rings to quickly establish the initial distribution of the uniform array. Subsequently, the optimisation process involves refining the element positions and numbers by imposing various constraints on factors such as the spacing increment between adjacent rings, the polar angle between adjacent elements on each ring, and the initial polar angle among adjacent elements, which significantly enhances optimisation efficiency. By utilising the peak sidelobe level in wide-angle scanning as the fitness function, the synthesis problem is transformed into an optimisation conundrum, which is addressed using the covariance matrix adaptation evolutionary strategy (CMA-ES). Finally, a series of numerical examples is presented to demonstrate the effectiveness and benefits of the proposed synthesis method.

An approach to improve cross-polarisation (XP) radiation in probe-fed microstrip antennas is presented in this letter. The notable XP associated with self-diplexing antennas caused by radiator multiplexing is discussed for the first time. In order to enhance the interport isolation and polarisation purity, the proposed antenna exploits the combined advantages of the defected ground structure (DGS) and shorting pin arrays. Experimental results show that the resonant frequencies of the self-diplexing antenna are 9.56 and 11.28 GHz, accompanied by a port-to-port isolation of more than 29.4 dB. The suppression of XP around 50° range on either side of the boresight in both principal planes is greater than 12.3 and 11.1 dB for ports 1 and 2, respectively.

This paper introduces a novel compact planar end-fire antenna system featuring circularly polarised (CP) radiation and zero ground clearance. The system is constructed using a half-wavelength TE_0.5,0 mode open waveguide, which inherently generates vertically polarised electric components. By incorporating a slow-wave (SW) structure in the form of metallised blind via holes within the waveguide, a significant SW effect is achieved, resulting in a 40% reduction in the waveguide longitudinal size compared to the conventional counterpart design. Furthermore, by etching an open-ended slot in the top metallic surface of the waveguide, the edges of its aperture are enabled to generate an electric dipole mode. In this way, the horizontally polarised electric components can be achieved without increasing the antenna's footprint. With the proper combination, the design can effectively achieve the required 90°-phase difference in the end-fire direction for these two components. Finally, a 2.5 GHz antenna was designed and optimised. Aiming to mimic the practical environment of mobile terminal devices, a large metallic ground was adopted under the antenna in the simulation and experiment. The measured results indicated that an overlapped impedance |S₁₁| < −10 and axial ratio (AR < 3) bandwidth of 2.7% from 2.495 to 2.562 GHz is achieved. The proposed antenna shows great potential in wireless communication applications for mobile terminal devices.

In this paper, a novel topology for the multiway filtering power divider with a large power dividing ratio (PDR) is proposed. A three-line coupled structure and a λ/2 open-ended stub are introduced between the input port and the power dividing junction to reduce the requirement for high impedance in the low-power path and obtain filtering performance. The PDR is significantly enhanced, and the out-of-band rejection is improved. Meanwhile, a wideband port-to-port isolation is achieved through the isolation network. To verify the validity of the proposed methodology, two three-way wideband filtering power dividers with the power ratios of 5:3:2 and 8:1:1 are designed. The simulated and measured results demonstrate that the 5:3:2 (8:1:1) power divider has the bandwidth of 51.57% (48.5%) with the return loss less than −15 dB and the isolation bandwidth 80% (102%) with reference to −20 dB.

As elastic electrical connectors, fuzz buttons provide a vertical and solderless electrical interconnection in microwave modules to enhance the integration. However, prolonged use in harsh environments poses a risk of potential failure in electronic components, potentially compromising communication system reliability. This work studies the impact of fuzz button degradation in harsh environments on analog modulation (AM) and pseudo random binary sequence (PRBS) signal transmission using theoretical analysis and experimental testing. Accelerated tests are designed to obtain the fuzz button samples with different degradation levels. The surface morphology observation and elemental analysis are conducted to analyse the degradation mechanism. In addition, a transmission channel with fuzz button interconnections is designed and the corresponding equivalent circuit model is developed. Based on the proposed circuit model, the effects of fuzz button degradation on the integrity of both AM signal and PRBS signal are investigated by analysing the metrics such as waveform, eye diagram and bit error rate (BER) of the output signal. In addition, the effects of the carrier frequency of AM signals, and the transmission rate of the PRBS signals on signal transmission are also investigated. The simulation results of the circuit model show good agreements with experimental tests. The research results provide a better understanding regarding the potentially corrosive effects of harsh environments on fuzz button connectors and the negative effects on the signal integrity. Moreover, the research results provide comprehensive data support for identifying key features that are used for the development of machine learning models for fault diagnosis and localisation in radio frequency (RF) circuits with fuzz button interconnections.

A Huygens source dielectric resonator antenna (DRA) with unidirectional radiation pattern is presented. It consists of a coaxial probe exciting a rectangular, homogeneous and uniaxial anisotropic dielectric resonator (DR). To obtain a Huygens source radiation pattern, a pair of quasi-TM and TE modes are combined by controlling the permittivity tensor of the DR. A prototype operating at 2.5 GHz has been designed. The DR is made up of periodic anisotropic unit cells on a subwavelength scale and fabricated using a three-dimensional (3-D) printer. The simulated and measured results are in reasonable agreement. A relative impedance bandwidth of $��12.5�\%�$ and a front-to-back ratio larger than 15 dB at operating frequency are finally measured.