This paper exhibits a high-gain, low-profile dipole antenna array (DAA) for 5G applications. The dipole element has a semi-triangular shape to realize a simple input impedance regime. To reduce the overall antenna size, a substrate integrated cavity (SIC) is adopted as a power splitter feeding network. The transition between the SIC and the antenna element is achieved by a grounded coplanar waveguide (GCPW) to increase the degree of freedom of impedance matching. Epsilon-near-zero (ENZ) metamaterial technique is exploited for gain enhancement. The ENZ metamaterial unit cells of meander shape are placed in front of each dipole perpendicularly to guide the radiated power into the broadside direction. The prospective antenna has an overall size of 2.58 λg3 and operates from 28.5 GHz up to 30.5 GHz. The gain is improved by 5 dB compared to that of the antenna without ENZ unit cells, reaching 11 dBi at the center frequency of 29.5 GHz. Measured and simulated results show a reasonable agreement.
The Ka band has found applications in satellite, and radar communications. It is also expected that this band will be utilized for 5G applications. This paper presents single-and double-beam microstrip reflectarrays with single layer and compact size for Ka band communications at 28 GHz. Three different unit cells are investigated in this paper. Single-and double-beam reflectarrays are investigated. The reflectarrays are designed at 28 GHz with a physical size of 10k 9 10k. A pyramidal horn antenna is used for the feeding purpose. The focal-length-to-diameter (F/D) ratio is equal to one. Two different scenarios for single-beam reflectarrays are presented: one with a broadside direction and the other with a 10°tilt angle. The simulation results show that for the broadside single-beam scenario, it is possible to achieve a gain up to 28.5 dB, and a 1-dB gain-bandwidth up to 30.7%. On the other hand, the presented reflectarray for the single-beam design at 10°tilt angle gives a gain of about 26.4 dB, a side lobe level (SLL) of about-15.6 dB, and a 19.3% gain-bandwidth. For the double-beam reflectarray, four different designs at different angles of 5°, 10°, 15°, and 20°have been simulated and compared. Moreover, the simulation results on the double-beam reflectarray show that the double-beam design at 10°is better from the gain and SLL perspectives. Two prototypes for broadside single-beam reflectarrays have been fabricated and measured. The measurement results show a good match with the simulation results. Gain flatness is guaranteed for both the simulated and measured results over the band of interest.
A quad-port multiple-input multiple-output (MIMO) filtenna with compact dimensions of 50 × 50 mm2 are configured, in which each element is placed orthogonally to its adjacent to enhance the isolation. The MIMO element is configured based on the novel COVID-19 virus shape with a co-planar waveguide feeding structure (CPW) and dimensions 17 × 22 mm2. The element bandwidth is ranging from 3.3 GHz to more than 60 GHz. Three frequency notches are designed at 3.5 GHz for WiMAX and 5.5 GHz for WLAN, and 8.5 GHz for X-band applications. A bandpass filter (BPF) with high out of band rejection is used as a decoupling structure (DS) to improve the isolation to more than 30 dB across most of the bandwidth. The equivalent circuit model is scrutinized to investigate the enactment of the decoupling structure. The proposed MIMO filtenna system provides an impedance bandwidth of 2.4-18 GHz, a peak gain of 13.2 dBi, and an envelope correlation coefficient (ECC) less than 0.00021. In turn, channel capacity loss does not exceed 0.2. The MIMO filtenna is fabricated and measured. Good agreement between the measured and simulation results is achieved.
Abstract-In this paper, a new technique is proposed for field effect transistor (FET) small-signal modeling using neural networks. This technique is based on the combination of the Mel frequency cepstral coefficients (MFCCs) and discrete sine transform (DST) of the inputs to the neural networks. The input data sets to traditional neural systems for FET small-signal modeling are the scattering parameters and corresponding frequencies in a certain band, and the outputs are the circuit elements. In the proposed approach, these data sets are considered as forming random signals. The MFCCs of the random signals are used to generate a small number of features characterizing the signals. In addition, other MFCCs vectors are calculated from the DST of the random signals and appended to the MFCCs vectors calculated from the signals. The new feature vectors are used to train the neural networks. The objective of using these new vectors is to characterize the random input sequences with much more features to be robust against measurement errors. There are two benefits for this approach: a reduction in the number of neural networks inputs and hence a faster convergence of the neural training algorithm and robustness against measurement errors in the testing phase. Experimental results show that the proposed technique is less sensitive to measurement errors than using the actual measured scattering parameters.
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