A new approach to enhance the bandwidth of Tightly Coupled Dipole Arrays (TCDA) is presented. The new design achieves the integration of a semi-resistive Frequency Selective Surface network (FSS) composed of a non-resistive low-pass FSS and two resistive band-stop FSSs. The integration of this FSS network within a dual-polarized Tightly Coupled Dipole Array (TCDA) led to an increased impedance bandwidth of 28:1 from 0.20GHz to 5.6GHz. Notably, the use of an FSS superstrate allowed for scanning down to 60∘ at VSWR < 3 in the E-plane and VSWR < 4 in the D-and H-planes. Additionally, the strategic use of the inserted low-pass FSS reduces the resistive effects above 2.5GHz for improved average efficiency. A 12 × 12 array prototype was fabricated and tested to verify the bandwidth and gain of a finite array. The simulated radiation efficiency was demonstrated to be 83%, on average, across the band.

This paper presents a 60 GHz millimeter-wave (mm-wave) antenna array using standard printed circuit board (PCB) for 3D Antenna-in-package (AiP) implementation. The array consists of a 4 microstrip patch elements, differentially fed with an open stub matching feed network to enable 3D integration. The $1\times 4$ finite antenna array with ball grid array (BGA) and silicon (Si) interposer operates from 58.46 to 62.14 GHz with 3.6 GHz instantaneous bandwidth, low mutual coupling of about <−25 dB and achieves a realized gain of about 10.51 dBi. The array is capable of scanning down to ±45° and provides low cross polarization levels of −40 dB. The fabricated multilayer $1\times 4$ array consists of two substrates and one bondply layer with antennas, via-to-open stub matching network, and a differential to single-ended corporate feed network for the measurement. A prototype with a differential to single-ended corporate feed network was fabricated and tested showing a gain of about 10.02 dBi at the operating frequency with $\geq 90$ % radiation efficiency. Such a gain and efficiency make the presented design a leading candidate for 3D AiP applications.

In this paper, we present a non-standalone dual-band shared aperture antenna array that operates at both the current Dedicated Short Range Communications (DSRC) band (viz. 5.9 GHz) and the future 5G millimeter-wave (mm-wave) band (viz. 28 GHz) allocated for vehicle-to-vehicle (V2V) communications. The design consists of a 2 $\times $ 2 differentially fed patch array operating at 5.9 GHz, co-printed with a 1 $\times $ 2 series fed array operating at 28 GHz on a single layer substrate. In particular, the shared aperture array operates from 5.84-5.94 GHz (DSRC band) with a 100 MHz impedance bandwidth and from 27.75 - 28.47 GHz (5G mm-wave band) with a 720 MHz impedance bandwidth. Gain improvement of ~1.44 dB at 28 GHz is observed as a result of the higher order modes generated by the 5.9 GHz array. The shared aperture array was fabricated and measured showing a gain of ~9.97 dBi at 5.9 GHz and ~12.3 dBi at 28 GHz. The ports of the array are highly isolated with measured isolation >55 dB at the DSRC band and >33 dB at the 5G mm-wave band.

This paper presents an intelligent design method for a corner-truncated microstrip patch antenna (CTMPA) operating at 32 GHz using various well-known machine learning (ML) techniques. Our objectives are to obtain a gain of >5 dBic across a 10% bandwidth, an axial ratio (AR) of <3 dB, and a return loss of <−10 dB. First, a dataset of 715 full-wave simulated samples is analyzed with four distinct antenna characteristics (viz. features), along with the related computed $|S_{11}|$, gain, and AR. Using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and R${}^2$ score, 12 ML regression models were examined to compare the training data with the new predicted values. Next, the model that best satisfies our objectives was chosen. Results showed that the artificial neural network (ANN) followed by k-nearest neighbor (KNN) regression produced the lowest error compared to all tested ML models. The design parameters that achieved our intended objectives were computed using the predicted results. The predicted design was validated using a full-wave simulation and a prototype measurement.