Researching on the Deterministic Channel Models for Urban Microcells Considering Diffraction Effects

Deterministic channel models, such as the three-dimensional (3D) ray launching method, can yield wireless channel parameters. In the non-line-of-sight (NLOS) propagation, the outdoor 3D ray launching method that considers diffraction effects is more accurate than the one that does not. While considering the diffraction effect, obtaining the diffraction point is challenging. This paper proposed a method for determining diffracted rays using the receiving sphere method in 3D ray launching. The diffraction point is determined using the shortest distance method between two straight lines, and the signal loss from the transmitting to receiving antennas is obtained. Furthermore, experiments on a millimeter wave in a microcell scenario were performed. The test results of the wireless channel parameters were compared with theoretical calculations. The results obtained via the 3D ray launching method that only considers the specular reflection and direct rays agree with the experimental results in the line-of-sight (LOS); furthermore, they generate larger errors compared with the experimental results in the NLOS. The results obtained via the 3D ray launching method that considers the direct ray, reflected rays, and diffracted rays agree with the experimental results both in the LOS and NLOS. Therefore, the 3D ray launching method that considers the diffraction effect can improve the prediction accuracy of the millimeter wave channel parameters in a microcell

Multiscale Decomposition Prediction of Propagation Loss for EM Waves in Marine Evaporation Duct Using Deep Learning

A tropospheric duct (TD) is an anomalous atmospheric refraction structure in marine environments that seriously interferes with the propagation path and range of electromagnetic (EM) waves, resulting in serious influence on the normal operation of radar. Since the propagation loss (PL) can reflect the propagation characteristics of EM waves inside the duct layer, it is important to obtain an accurate cognition of the PL of EM waves in marine TDs. However, the PL is strongly non−linear with propagation range due to the trapped propagation effect inside duct layer, which makes accurate prediction of PL more difficult. To resolve this problem, a novel multiscale decomposition prediction method (VMD−PSO−LSTM) based on the long short−term memory (LSTM) network, variational mode decomposition (VMD) method and particle swarm optimization (PSO) algorithm is proposed in this study. Firstly, VMD is used to decompose PL into several smooth subsequences with different frequency scales. Then, a LSTM−based model for each subsequence is built to predict the corresponding subsequence. In addition, PSO is used to optimize the hyperparameters of each LSTM prediction model. Finally, the predicted subsequences are reconstructed to obtain the final PL prediction results. The performance of the VMD−PSO−LSTM method is verified by combining the measured PL. The minimum RMSE and MAE indicators for the VMD−PSO−PSTM method are 0.368 and 0.276, respectively. The percentage improvement of prediction performance compared to other prediction methods can reach at most 72.46 and 77.61% in RMSE and MAE, respectively, showing that the VMD−PSO−LSTM method has the advantages of high accuracy and outperforms other comparison methods