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Holography has garnered an explosion of interest in tremendous applications, owing to its capability of storing amplitude and phase of light and reconstructing the full-wave information of targets. Spatial light modulators, metalenses, metasurfaces, and other devices have been explored to achieve holographic images. However, the required phase distributions for conventional holograms are generally calculated using the Gerchberg–Saxton algorithm, and the iteration is time-consuming without Fourier transform or other acceleration techniques. Few studies on designing holograms using artificial intelligence methods have been conducted. In this Letter, a three-dimensional (3D)-printed hologram for terahertz (THz) imaging based on a diffractive neural network (DNN) is proposed. Target imaging letters “THZ” with uniform field amplitude are assigned to a predefined imaging surface. Quantified phase profiles are primarily obtained by training the DNN with the target image and input field pattern. The entire training process takes only 60 s. Consequently, the hologram, that is, a two-dimensional array of dielectric posts with variational heights that store phase information, is fabricated using a 3D printer. The full-wave simulation and experimental results demonstrate the capability of the proposed hologram to achieve high-quality imaging in the THz regime. The proposed lens and design strategy may open new possibilities in display, optical-data storage, and optical encryption.

Abstract-In this paper, a novel source localization scheme is proposed based on the unitary ESPRIT algorithm with back ray tracing technique and the city electronic maps. Our scheme can be summarized into two steps. First, the unitary ESPRIT algorithm is employed to estimate the angles and delays of the arrival rays radiated from the source. Second, based on the obtained information we devise a back ray tracing technique to recover the signal propagation paths according to the Geometrical Theory of Reflections and the city electronic map. After these two steps the source position can be obtained by averaging all the estimated positions. In order to minimize estimated errors caused by the Unitary ESPRIT, a valid-range selection criterion for the judgment of the validity of the estimated position data is proposed. On the other hand, we introduce a path length weighting factor to reduce the estimated errors caused by the terrain data inaccuracy. This position method can locate both the line of sight (LOS) and non-line of sight (NLOS) sources efficiently and it also can locate multi-sources simultaneously. Six simulations are carried out in three terrain scenarios. The numerical results demonstrate that our model can be applied to estimate the positions for both 2D and 3D cases. The accuracy of our model for a cell of 80 m × 45 m can reach 10 m when SNR is greater than 10 dB.

Abstract-A novel multiport matching method is devised to directly maximize the mean capacity with rigorous consideration of the mutual coupling effects of the matching network. In the RF front end of the real communication circuits, the mutual couplings always exist. In this paper, 1) a theoretical capacity upper bound of the 2-by-2 MIMO system with a matching network using the water-filling as the power allocation rule is analytically derived for the first time, 2) the Genetic Algorithm is employed to optimize the parameters of the matching network for the maximization of the mean capacity, 3) a coupled microstrip lines structure is devised to implement the matching network of the real MIMO receiving circuits by this matching method. The numerical results in the last section demonstrate that an optimized matching network obtained using our novel MPM method is capable to enhance the performance of the MIMO systems in a range of different indoor environments. This verifies that our method is not only effective but also practical.