One of the key parameters in radio link planning is the propagation path loss. Most of the existing methods for its prediction are not characterized by a good balance between accuracy, generality, and low computational complexity. To address this problem, a machine learning approach for path loss prediction is presented in this study. The novelty is the proposal of a compound model, which consists of two regression models and one classifier. The first regression model is adequate when a line-of-sight scenario is fulfilled in radio wave propagation, whereas the second one is appropriate for non-line-of-sight conditions. The classification model is intended to provide a probabilistic output, through which the outputs of the regression models are combined. The number of used input parameters is only five. They are related to the distance, the antenna heights, and the statistics of the terrain profile and line-of-sight obstacles. The proposed approach allows creation of a generalized model that is valid for various types of areas and terrains, different antenna heights, and line-of-sight and non line-of-sight propagation conditions. An experimental dataset is provided by measurements for a variety of relief types (flat, hilly, mountain, and foothill) and for rural, urban, and suburban areas. The experimental results show an excellent performances in terms of a root mean square error of a prediction as low as $7.3$ dB and a coefficient of determination as high as 0.702. Although the study covers only one operating frequency of 433 $\mathrm{M}$$\mathrm{Hz}$, the proposed model can be trained and applied for any frequency in the decimeter wavelength range. The main reason for the choice of such an operating frequency is because it falls within the range in which many wireless systems of different types are operating. These include Internet of Things (IoT), machine-to-machine (M2M) mesh radio networks, power efficient communication over long distances such as Low-Power Wide-Area Network (LPWAN)—LoRa, etc.

In this paper is presented automatic antenna pattern measurement system. The system provides the opportunity to measure different types of antennas in different frequency ranges. The measured pattern can be presented in Cartesian, polar or 3D spherical coordinate system.

In this paper we propose an approach for interference recognition and frequency channel identification based on the analysis of the large amount of data obtained from long term spectrum monitoring. We illustrate the approach with an example of the detection of a specific type of interference in the uplink of a 3G network caused by the effect “ducting”. Further, using Hjorth parameters for the creation of a set of robust feature descriptor, we show how the dimensionality of the space of the primary parameter data could be reduced and apply it to cluster analysis for interference and channel identification.

This paper researches a novel 2D Z-shaped Electromagnetic Band-Gap (EBG) structure, its dispersion diagram and application field. Based on a transmission line model, the dispersion equation is derived and theoretically investigated. In order to validate theoretical results, a full wave analysis is performed and the electromagnetic properties of the structure are revealed. The theoretical results show good agreement with the full wave simulation results. The frequency response of the structure is compared to the well know structures of Jerusalem cross and patch EBG. The results show the applicability of the proposed 2D Z-shaped EBG in microstrip patch antennas, microstrip filters and high speed switching circuits, where the suppression of parasitic surface wave is required.

This article studies methods for channel activity analysis in cognitive radio that do not require a priori information about the signal, the channel and the noise power. Channel identification is a great challenge for cognitive devices, so channel activity plays an important role in spectrum management decisions. Dimension reduction with Principal Component Analysis (PCA) and various clustering methods (agglomerative, K-means and K-medoids) are used to reduce the hardware and software requirements while maintaining and improving detection accuracy. The goal of the proposed analysis is to discover the optimal parameters for data processing in spectrum hole allocation for cognitive radio systems by using preprocessing and cluster analysis.