Petr Horky’s research while affiliated with Brno University of Technology and other places

The spatial statistics of radio wave propagation in specific environments and scenarios, as well as being able to recognize important signal components, are prerequisites for dependable connectivity. There are several reasons why in-vehicle communication is unique, including safety considerations and vehicle-to-vehicle/infrastructure communication.The paper examines the characteristics of clustering power delay profiles to investigate in-vehicle communication. It has been demonstrated that the Saleh-Valenzuela channel model can also be adapted for in-vehicle communication, and that the signal is received in clusters with exponential decay. A measurement campaign was conducted, capturing the power delay profile inside the vehicle cabin, and the reweighted l1 minimization method was compared with the traditional k-means clustering techniques.

This paper introduces an approach to process channel sounder data acquired from Channel Impulse Response (CIR) of 60GHz and 80GHz channel sounder systems, through the integration of Long Short-Term Memory (LSTM) Neural Network (NN) and Fully Connected Neural Network (FCNN). The primary goal is to enhance and automate cluster detection within peaks from noised CIR data. The study initially compares the performance of LSTM NN and FCNN across different input sequence lengths. Notably, LSTM surpasses FCNN due to its incorporation of memory cells, which prove beneficial for handling longer series.Additionally, the paper investigates the robustness of LSTM NN through various architectural configurations. The findings suggest that robust neural networks tend to closely mimic the input function, whereas smaller neural networks are better at generalizing trends in time series data, which is desirable for anomaly detection, where function peaks are regarded as anomalies.Finally, the selected LSTM NN is compared with traditional signal filters, including Butterworth, Savitzky-Golay, Bessel/Thomson, and median filters. Visual observations indicate that the most effective methods for peak detection within channel impulse response data are either the LSTM NN or median filter, as they yield similar results.

In this letter, we examine the effect of misalignment angle on cluster-based power delay profile (PDP) modeling for a 60 GHz millimeter-wave uplink. The analysis uses real-world data, where fixed uplink scenarios are realized by placing the transmitter at ground level and the receiver at the building level. Both outdoor-to-indoor (O2I) and outdoor-to-outdoor (O2O) scenarios are studied. Using the misalignment angle and the scenario as inputs, we propose a statistical PDP simulation algorithm based on the Saleh-Valenzuela model. Different goodness-of-fit metrics reveal that our proposed algorithm is robust to both O2I and O2O scenarios and can approximate the PDPs fairly well, even in case of misalignment.

In this paper, we analyze the spectral efficiency for millimeter wave downlink with beam misalignment in urban macro scenario. For this purpose, we use a new approach based on the modified Shannon formula, which considers the propagation environment and antenna system coefficients. These factors are determined based on a multi-ellipsoidal propagation model. The obtained results show that under non-line-of-sight conditions, the appropriate selection of the antenna beam orientation may increase the spectral efficiency in relation to the direct line to a user.

In this paper, we examine the effect of misalignment angle on cluster-based power delay profile modeling for a 60 GHz millimeter-wave (mmWave) uplink. The analysis uses real-world data, where fixed uplink scenarios are realized by placing the transmitter at ground level and the receiver at the building level. Both outdoor-to-indoor (O2I) and outdoor-to-outdoor (O2O) scenarios are studied. Using the misalignment angle and the scenario as inputs, we propose a statistical power delay profile (PDP) simulation algorithm based on the Saleh-Valenzuela (SV) model. Different goodness-of-fit metrics reveal that our proposed algorithm is robust to both O2I and O2O scenarios and can approximate the PDPs fairly well, even in case of misalignment.