The Active Reflection Coefficient (ARC) is a key factor in designing electrically small array antennas, as it accounts for the antenna's performance in a use-case. Accurate modeling of the ARC requires the complete set of scattering parameters, which requires a full-wave simulation. In this paper, we investigate how to accelerate full-wave frequency domain simulations by using the Loewner framework with adaptive frequency sampling, to accurately model the ARC. Here we compare interpolation on the ARC data directly with interpolation on the scattering parameters from which the corresponding ARC is determined, and interpolation on the scattering parameters, but with adaptive frequency sampling according to the estimated ARC error. The three strategies are tested on two cases, one 2 × 2 IoT array, and one 10 × 10 BoR Array. In both antenna cases, the latter strategy for accurately modeling the ARC yields the most accurate model for the least number of interpolation points used.

Large array antennas, constructed from disjoint irregular sub arrays, where each sub array port is digitally con-trolled, are of growing interest for communication and sensor applications. This paper investigates two different 28x30 antenna arrays, both operating as isophoric arrays (equal amplitude excitation), designed to support pencil beams across a large scan window. We focus on how various embedded element pattern approximations influence the optimization outcomes in terms of hexomino-based sub arrays and their effect on the peak sidelobe level (pSLL). Our analysis shows that commonly used embedded pattern approximations can cause peak sidelobe level deviations of up to 1.4 dB. Additionally, we demonstrate properties of a simplified and fast cost function to assess the pSLL in these arrays.

A new, accelerated MoM solver is presented and it is here used to investigate the convergence of active element impedance. The results show that the active impedance of the center element in a sufficiently large finite array is close to that of an element in an infinite array, as is known. The size requirement for this to hold will be investigated and compared to previously published works. The theory and methods behind the effective MoM solver will also be presented.

Frequency domain sweeps of array antennas are well-known to be time-intensive, and different surrogate models have been used to improve the performance. Data-driven model order reduction algorithms, such as the Loewner framework and vector fitting, can be integrated with adaptive frequency sampling algorithms, in an iterative scheme, to reduce the number of full-wave simulations required to accurately capture the requested frequency behavior of multiport array antennas. In this work, we propose two novel adaptive methods exploiting a block matrix function which is a key part of the Loewner framework generating system approach. The first algorithm leverages an inherent matrix parameter freedom in the block matrix function to identify frequency points with large errors, whereas the second utilizes the condition number of the block matrix function. The first method effectively provide a frequency domain error estimator, which is essential for improved performance. Numerical experiments on multiport array antenna S-parameters demonstrate the effectiveness of our proposed algorithms within the Loewner framework, where the proposed algorithms reach the smallest errors for the smallest number of frequency points chosen.

Tightly coupled arrays are a class of antenna arrays that are very wideband and have applications in communication and sensor systems. In both applications, low side-lobe levels are of interest. However, as tightly coupled arrays have high inter-element coupling, care must be taken when finding a suitable set of excitation coefficients. To demonstrate this, we design a tightly coupled array. Second, we extend a previously published convex optimization algorithm to account for strong inter-element coupling. This optimization algorithm solely finds excitations implementable in a simplified hardware model. The results are compared with conventional taperings, and show that our algorithm can achieve an excitation with lower reflected power compared to the conventional taperings, while maintaining similar gain and side-lobe levels.

This paper introduces a novel filtering approach that employs integrated periodic structures with a conventional Vivaldi antenna to achieve a fully integrated bandpass filtering antenna. The approach results in a wide out-of-band suppression, high passband selectivity, adjustable operational bandwidth, and low insertion loss. The proposed filtering approach maintains the original size of the conventional Vivaldi antenna (base antenna) without requiring additional modifications. To validate the approach, we present two filtering Vivaldi antennas: filtering antenna I (center frequency: 18GHz, fractional bandwidth: 21%, insertion loss: 0.32dB) and filtering antenna II (center frequency: 6.5GHz, fractional bandwidth: 12%, insertion loss: 0.6dB). Their wide out-of-band gain suppression (typically >= 15dB) covers the conventional Vivaldi antenna's frequency range (4-24GHz). A prototype of the filtering antenna I is manufactured. Its measurement results validate the proposed approach and show good agreement with the simulated reflection coefficient, realized gain, and radiation patterns. The features of the proposed filtering antenna approach, make it suitable for various applications requiring efficient frequency filtering.

A convex optimization program is presented for wideband arrays. Constraints are imposed on the frequency variation of excitation coefficients to ensure that the optimal solution can be realized in a wideband active electronically scanned array (AESA). AESA implementation with true time delays (TTDs) and phase shifters are handled separately. We also discuss the general case of combining TTDs and phase shifters. Contrary to single-frequency optimization, the wideband optimization method presented here ensures that the computed excitation is optimal over a specified bandwidth. It is shown that there is a tradeoff between instantaneous bandwidth and sidelobe level. The proposed method works for both narrow and wideband arrays, as illustrated with examples. In addition to regular arrays, the method is also applicable to monopulse arrays. The optimization program is implemented in terms of embedded element patterns (EEPs) to account for and compensate for mutual coupling, radome, and platform effects.

The near-field contribution in the high-frequency method Shooting and Bouncing Rays (SBR) is investigated for installed antenna performance. Three SBR solvers, the in-house solver SIENT and two commercial solvers, are compared to a commercial full-wave solver. SIENT and one commercial solver have an option to disable near-field effects, which allows the strength of these effects to be investigated. The last solver also includes near-field effects. The results indicate an increase in accuracy when including near-field effects. The most notable difference in the far-field phase and the induced surface current are obtained for the in-house solver. Improvements for the surface current density is seen close to the antenna. On the tested platform, the difference in gain is not as notable but it is overestimated for both the in-house solver and the commercial solvers when excluding near-field terms.

This paper presents a novel active integrated antenna unit cell for millimeter-wave (mmW) active array designs. With the co-design of the electromagnetics and circuits, a 47% power added efficiency has been achieved over a 5 G band around 28 GHz. The direct matching network is simple and compact, which is easy to be allocated in the antenna unit cell. Moreover, the impedance bandwidth is also stable versus beam scanning in various directions. Hence, the investigated deep integration and direct matching technologies have significantly enhanced the system power efficiency, paving the way for high efficiency mmW communication systems.

In Internet of Things (IoT) applications the antennas are often electrically small at their radiation frequency. This makes it hard to design antennas that meet desired bandwidth requirements. It is therefore interesting to consider the trade-off between antenna size and antenna position within the terminal with respect to its available bandwidth, as characterized by the Q-factor bound. To determine these quantities are associated with solving a non-trivial optimization problem. Recent development of model order reduction techniques can include parametric dependencies. Here we apply the data-driven Loewner framework to the Q-factors as a function of size and position parameters, to investigate how well these methods work for the Q-factor bounds in IoT applications. We show that the Loewner framework can deliver a reduced-order model of the bound. Properties of the reduced model can be used to indicate how well the reduced order interpolates the parametric dependence.