This paper presents a method for predicting atmospheric duct conditions from a clutter power spectrum using deep learning. To accurately predict the duct conditions, deep learning with a binary classification is applied to the proposed refractivity from the clutter (RFC) method. The input data set is the artificial clutter data that are generated via the Advanced Refractive Prediction System (AREPS) simulation software Ver. 3.6 in conjunction with random atmospheric refractive indices. The output of the RFC method is then predicted via binary classification, indicating whether the atmospheric conditions are duct or non-duct. For the cross-validation, the clutter power spectrum data are generated based on real atmospheric refractivity data. The results show that the DNN trained with 5600 pieces of data (validation accuracy of 95.99%) exhibits a binary classification accuracy of 98.36%. The deep neural network (DNN) trained with 28,000 pieces of data (validation accuracy of 98.20%) achieves a binary classification accuracy of 99.06% with an F1-score of 0.9921.

In this paper, we propose a method for determining the optimal base station deployment to establish a stable communication environment for urban air mobility (UAM) operation in urban areas. To realistically model the UAM operating environment, we utilize DEM files that include data on terrain and buildings. Furthermore, the radiation patterns of the BS’s antenna are generated according to the 3GPP-standard, and the receiver antenna patterns are obtained through analysis of the UAM’s mounted antenna. The proposed method uses binary chromosomes to determine the locations of BSs and the orientations of the antennas. A genetic algorithm is then used to determine the optimal deployment of the base stations. When applying the proposed method to optimize BS deployment in the UAM corridor (area of approximately 5.4 km ${}^{\mathbf {2}}$ ), the optimization process resulted in the deployment of two base stations at 3.5 GHz, achieving an average received power of −61.36 dBm across the corridor. At 5.4 GHz, three base stations are deployed, with an average received power of −63.74 dBm. To validate the reliability of the simulation, measurements are conducted in a real urban environment, comparing the results from measurement, theory, and simulation. The received power values obtained from measurement, theory, and simulation are −110.21 dBm, −110.82 dBm, and −110.38 dBm, respectively. These results demonstrate that the proposed BS deployment method can be used to establish a stable communication environment in a real UAM corridor.

This paper proposes a receiving array antenna with a trapezoidal configuration that combines multiple rectifier circuits for wide-angle reception. The proposed receiving antenna system consists of three antenna elements operating at 5.8 GHz and the same number of rectifier circuits. To resolve the degradation in power transmission efficiency caused by misalignment, the receiving array antenna is designed in a trapezoidal configuration, and a quasi-air substrate is adopted for each antenna to achieve a high gain with the required beamwidth. All antenna elements in this system have measured reflection coefficients of less than -10 dB, and the overall beam coverage of the system is 205°. The power measurements of the receiving system are performed at 5° intervals within the beam coverage, and the results demonstrate that power is reliably received within the beam coverage through the proposed receiving antenna system.

In this paper, we propose a signal intelligence (SIGINT) drone swarm system with a three-dimensional (3-D) volumetric self-complementary array configuration. In the proposed system, multiple drones form two array layers separated along the boresight direction of the system, providing sufficient spacing between drones mounting an antenna element. The antenna elements in one array layer are arranged in a complementary manner to fill empty spaces in the other layer, allowing the system to maximize the number of drones deployed within the aperture area. As a result, the effective electrical spacing at 300 MHz is reduced from 1.7λ and 0.9λ to 0.85λ and 0.45λ along the x- and y-axes, respectively. The array gains of the proposed system are 3.96 dBi, 6.40 dBi, and 15.3 dBi at 100 MHz, 200 MHz, and 300 MHz, and the side-lobe levels (SLLs) are −13.0 dB, −12.7 dB, and −13.0 dB. In addition, the proposed drone swarm SIGINT system is evaluated in a practical SIGINT environment that considers terrain features, and then the detection performance is compared with those of conventional ground-based and airborne SIGINT systems. In this SIGINT scenario, the proposed system can detect signals over an extended detection range of 150 km than those of ground-based and airborne systems.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)