Ústav radioelektroniky

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DVB-T2 MISO System Influenced by I/Q-Errors in Mobile and Portable Transmission Scenarios
(IEEE, 2025-05-12) Polák, Ladislav; Buchta, Šimon; Svoboda, Marek; Semenov, Dmitrii; Šotner, Roman; Petržela, Jiří; Kratochvíl, Tomáš
The Second Generation Digital Video Broadcasting Terrestrial (DVB-T2) system is the only DVB standard that supports the multiple-input single-output (MISO) transmission technique. This paper explores the performance of the DVB-T2 MISO system under laboratory conditions, focusing mainly on the impact of imperfections in the Orthogonal Frequency-Division Multiplexing (OFDM) modulator, referred to as I/Q-errors. Next, special broadcast channel conditions are emulated using different channel models created for mobile and portable transmission scenarios. The measurement setup is designed for flexibility, enabling easy interchangeability of measurement equipment (e.g., TV signal analyzers) and set-top boxes (STBs). The results indicate that different professional measurement instruments measure the performance of DVB-T2 MISO systems in terms of objective parameters with different accuracy.
Cost-Effective Measurement Setup for Analyzing Signal Coverage in 4G/5G Mobile Networks
(IEEE, 2025-05-12) Polák, Ladislav; Baránek, Michal; Kufa, Jan; Šotner, Roman; Dluhá, Jitka
Long-term monitoring, measurement, and collection of key performance indicators (KPIs) for fourth and fifthgeneration (4G and 5G) networks under various transmission conditions are essential for optimizing performance and ensuring seamless connectivity in dense environments. In this paper, we present a cost-effective, portable measurement setup with an intuitive user interface, designed for efficient cost-efficient measurements in diverse environments. This setup was used for large-scale measurement studies of 4G (Long-Term Evolution - LTE) and 5G (Non-Standalone - NSA) mobile networks conducted in Brno, Czechia, enabling the exploration of indoor and outdoor network coverage across different radio frequency (RF) bands and environmental conditions. Using this equipment, nearly 1,500,000 samples were collected with a time resolution of 1 second. To support the reproducibility of our study, the dataset is publicly available for download. The results demonstrate varying performance of 4G and 5G mobile networks across different scenarios and show the significant impact of network load fluctuations based on the time of day.
Exploring Deep Learning Architectures for RF Signal Classification
(IEEE, 2025-05-12) Polák, Ladislav; Turák, Samuel; Šotner, Roman; Kufa, Jan; Maršálek, Roman; Dhaka, Arvind
Future 6G radio networks will heavily rely on deep learning (DL) models for both signal and data processing. DL-based solutions can be highly effective in classifying various radio frequency (RF) signals influenced by noise or intentional jamming as they are capable of recognizing patterns even under challenging conditions. This paper focuses on the classification of different RF signals using three DL-based models: CNN, GRU, and CGDNN. For this purpose, a dataset containing RF signals influenced by various impairments (e.g., I/Q-imbalance) and transmission conditions (e.g., multipath propagation) was created using MATLAB. Both the dataset and the source code have been made publicly available to support further research in this area. Preliminary results shown that the performance of DL-based approaches depends not only on the RF impairments considered but also on the preparation of the dataset.
Fractional-Order Equivalent Circuit Representation of Sucrose Solutions Electrical Impedance Measured in Bipolar Configuration
(IEEE, 2025-03-30) Duckworth, Colin; Šotner, Roman; Jeřábek, Jan; Freeborn, Todd
In this work, the alteration in electrical impedance of a liquid (unsweetened FuzeTea) with varying increases in sucrose concentrations (up to 785 mM) was investigated. The aim was to further understand how changes in liquid solutions with adulterants (in this case sucrose) change the electrical impedance as a potential method for food quality monitoring. The electrical impedance was measured from 100 Hz to 1 MHz using gold and platinum electrodes, then numerical optimization was applied to collected datasets to estimate the model parameters of a fractional-order equivalent circuit model to best represent the data. This model demonstrated less than 2% impedance magnitude and less than 6% impedance phase deviation compared to the experimental data. Overall, the model parameter representing the liquid resistance showed large increases for increasing concentrations of sucrose, suggesting that it may be a potential marker for assessing sucrose adulterants in liquid solutions.
A Comprehensive Evaluation of Deep Vision Transformers for Road Extraction from Very-high-resolution Satellite Data
(Elsevier, 2025-01-02) Bolcek, Jan; Gibril, Mohamed Barakat A.; Al-Ruzouq, Rami; Shanableh, Abdallah; Jena, Ratiranjan; Hammouri, Nezar; Sachit, Mourtadha Sarhan; Ghorbanzadeh, Omid
Transformer-based semantic segmentation architectures excel in extracting road networks from very-high-resolution (VHR) satellite images due to their ability to capture global contextual information. Nonetheless, there is a gap in research regarding their comparative effectiveness, efficiency, and performance in extracting road networks from multicity VHR data. This study evaluates 11 transformer-based models on three publicly available datasets (DeepGlobe Road Extraction Dataset, SpaceNet-3 Road Network Detection Dataset, and Massachusetts Road Dataset) to assess their performance, efficiency, and complexity in mapping road networks from multicity VHR satellite images. The evaluated models include Unified Perceptual Parsing for Scene Understanding (UperNet) based on the Swin transformer (UperNet-SwinT), and Multi-path Vision Transformer (UperNet-MpViT), Twins transformer, Segmenter, SegFormer, K-Net based on SwinT, Mask2Former based on SwinT (Mask2Former-SwinT), TopFormer, UniFormer, and PoolFormer. Results showed that the models recorded mean F-scores (mF-score) ranging from 82.22% to 90.70% for the DeepGlobe dataset, 58.98% to 86.95% for the Massachusetts dataset, and 69.02% to 86.14% for the SpaceNet-3 dataset. Mask2Former-SwinT, UperNet-MpViT, and SegFormer were the top performers among the evaluated models. The Mask2Former, based on the SwinT, demonstrated a strong balance of high performance across different satellite image datasets and moderate computational efficiency. This investigation aids in selecting the most suitable model for extracting road networks from remote sensing data.

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