A 5G metasurface (MS) transmitarray (TA) feed by compact-antenna array with the performance of high gain and side-lobe level (SLL) reduction is presented. The proposed MS has two identical metallic layers etched on both sides of the dielectric substrate and four fixed vias connecting two metallic layers that works at 28 GHz to increase the transmission phase shift range. The proposed planar TA consisting of unit cells with different dimensional information can simulate the function as an optical lens according to the Fermat’s principle, so the quasi-spherical wave emitted by the compact Potter horn antenna at the virtual focal point will transform to the quasi-plane wave by the phase-adjustments. Then, the particle swarm optimization (PSO) is introduced to optimize the phase distribution on the TA to decrease the SLL further. It is found that the optimized TA could achieve 27 dB gain at 28 GHz, 11.8% 3 dB gain bandwidth, −30 dB SLL, and aperture efficiency of 23% at the operating bandwidth of 27.5–29.5 GHz, which performs better than the nonoptimized one. The advanced particularities of this optimized TA including low cost, low profile, and easy to configure make it great potential in paving the way to 5G communication and radar system.
In this article, a high gain transmittarray antenna for 5G is presented. The transmittarray antenna is composed of an ultra‐thin transmitarray and its feed structure of the filtering dielectric antenna. The ultra‐thin transmitarray based on cross‐shaped metasurface is designed to realize the gradient distribution of phase covering the range of 0~2 π. The compact low temperature co‐fired ceramic dielctric antenna array feed by subtrate integrated waveguide powder divider is exploited as the feed of the transmittarray. The experimental results show the metasurface of the structure has obvious convergence effect on the radiation energy of the antenna, and the directivity of the antenna is greatly enhanced. The sidelobe energy is effectively suppressed and the peak gain of the antenna is increased from 11.47 dB to 22.8 dB at the central frequency of 28.0 GHz. The high gain and small size make the transmittarray antenna suitable for application in 5G wireless communication.
In recent years, extreme learning machine (ELM) and its improved algorithms have been successfully applied to various classification and regression tasks. In these algorithms, MSE criterion is commonly used to control training error. However, MSE criterion is not suitable to deal with outliers, which can exist in general regression or classification tasks. In this paper, a novel extreme learning machine under minimum information divergence criterion (ELM-MinID) is proposed to deal with the training set with noises. In minimum information divergence criterion, the Gaussian kernel function and Euclidean information divergence are utilized to substitute the mean square error (MSE) criterion to enhance the antinoise ability of ELM. Experimental results on two synthetic datasets and eleven benchmark datasets show that this method is superior to traditional ELMs.
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