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Radioengineering

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Proceedings of Czech and Slovak Technical Universities

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September 2024, Volume 33, Number 3 [DOI: 10.13164/re.2024-3]

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A. Atanaskovic, A. Djoric, N. Males Ilic, B. P. Stosic, D. Budimir [references] [full-text] [DOI: 10.13164/re.2024.0339] [Download Citations]
Linearization of the Fifth Generation Power Amplifiers by Injection of the Second Order Digitally Processed Signals

This paper conducts a validation of two linearization approaches that utilize baseband nonlinear linearization signals of the 2nd order, through practical experiments on an asymmetrical two-way microstrip Doherty amplifier (ADA), and simulations on a symmetrical two-way Doherty amplifier (DA) as well as the single stage power amplifier (PA) for the post-OFDM 5G modulation formats. In the first approach, linearization signals are led at the input and output of the carrier transistor in the DA, while in the second approach, they are injected at the outputs of both the carrier and peaking amplifiers. The DA was tested in simulation for the FBMC signal of 20 MHz bandwidth, while the experimental measurements were performed for the FBMC signal on the ADA for different useful signal frequency bandwidths, 5 MHz, 7.5 MHz, and 10 MHz. The maximal improvement of DA linearity obtained in simulation is 10 dB for lower power and 5 dB for maximum amplifier output power, while the second approach gives around 2 dB better results for higher power levels. The experimental test for ADA performed for considered signal bandwidths indicates 3 dB to 5 dB linearity improvement for the implemented approaches and more symmetrical results achieved by the second approach. Additionally, the simulation tests for the PA were carried out for the FBMC, UFMC, and FOFDM signals of 100 MHz bandwidth, with the application of the first linearization approach. The minimal achieved linearization improvement is 13 dB for the FOFDM signal and a maximal of 18 dB for the FBMC signal.

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Keywords: The post-OFDM 5G modulations, broadband power amplifier, Doherty amplifier, baseband signal, second harmonic, linearization

S. Kittiwittayapong, D. Torrungrueng, K. Phaebua, K. Sukpreecha, T. Lertwiriyaprapa, P. Janpugdee [references] [full-text] [DOI: 10.13164/re.2024.0349] [Download Citations]
Miniaturization of Power Dividers by Using Asymmetric CMRC Structures and QWLTs with Low-Cost Materials

This paper presents the miniaturization of power dividers using asymmetric compact-microstrip-resonant-cell (CMRC) structures employing low-cost materials based on a quarter-wave-like transformer (QWLT). The proposed CMRC-based QWLT power divider is intended for operation at a frequency of 2.4 GHz, utilizing the FR-4 print circuit board (PCB) with a dielectric constant of 4.3 and a substrate thickness of 1.6 mm. The CMRC dimensions include a width of 5.32 mm and a length of 8.52 mm. It is found that a significant 50% size reduction of length is achieved compared to a conventional power divider, while maintaining an insertion loss (IL) of 3.3 dB, as well as achieving the return loss and isolation loss of 20 dB.

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Keywords: Power divider, quarter-wave transformer (QWT), quarter-wave-like transformer (QWLT), compact microstrip resonant cell (CMRC)

Z. Wang, D. Zhang, M. Gao, H. Liu, S. Fang [references] [full-text] [DOI: 10.13164/re.2024.0358] [Download Citations]
Microstrip Circularly-Polarized Leaky-Wave Antenna with Wide Axial Ratio Bandwidth for X-Band Application

A microstrip circularly polarized (CP) leaky-wave antenna (LWA) operating in the X-band, and having the characteristics of a broad axis-ratio bandwidth is proposed. The proposed LWA is made up of 13 unit cells in series through microstrip feeding lines. Elliptical and rectangular slots are etched in each unit cell to achieve the radiation of CP waves. The open stopband at the broadside frequency can be suppressed by shifting the feeding line position and etching two circular notches on both sides of each radiation patch. To validate the proposed method, a prototype antenna operating in the X-band is manufactured and measured. The measured result demonstrates that the −10-dB impedance bandwidth of the microstrip CP LWA is 42.2% (7.96-12.22 GHz); the 3-dB axial ratio bandwidth is 26.4% (9.2-12.2 GHz); the gain of the antenna is 16.0 dBic. Besides, the main beam maintains good CP radiation properties while it continues to scan from −22° to +18°.

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Keywords: Leaky-wave antenna, circular polarization, open stopband, axial ratio bandwidth

Z. Wang, Q. Wang, X. Dang [references] [full-text] [DOI: 10.13164/re.2024.0368] [Download Citations]
Altitude Range and Throughput Analysis for Directional UAV-assisted Backscatter Communications Networks

In the realm of Internet of Things (IoT) networks, Backscatter communication (BackCom) is a promising technique that allows devices to send data through the reflecting surrounding radio frequency (RF) signals. Integrating unmanned aerial vehicles (UAVs) with BackCom technology to establish UAV-assisted BackCom networks presents an opportunity to provide self-generated RF signals for backscatter devices, establishing self-sustaining data collection systems. This paper investigates directional UAV-assisted BackCom networks where UAVs are equipped with directional antennas, which differs from previous studies that mainly consider omni-directional antennas. To ensure the quality of BackCom, we develop a theoretical model that analyzes the valid altitude range of UAVs, which is often ignored in previous studies. Based on the altitude range of UAVs, we then derive the throughput of directional UAV-assisted BackCom networks. Extensive simulations are conducted to verify our theoretical model, revealing correlations between the UAV altitude range, the throughput, directional antennas, and other key parameters. Results indicate that UAVs need to set the proper UAV altitude according to multiple parameters to ensure successful communication. In addition, adjusting the beamwidth of directional antennas can enhance both the altitude range of UAVs and the throughput of networks.

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Keywords: Backscatter communications, UAV-assisted networks, directional antennas, altitude range, throughput

Y. Feng, J. Nie, G. Xie, H. Lv [references] [full-text] [DOI: 10.13164/re.2024.0376] [Download Citations]
Soil Moisture Content Inversion by Coupling AEA and ARMA

This study aimed to explore the inversion method of soil moisture content by using numerical simulation and field detection. The researchers used the early signal amplitude envelope (AEA) method to directly invert soil moisture in the shallow part of the soil, which avoided the transmission error of the Topp formula. The Auto-Regressive Moving Average Model (ARMA) was used to calculate the power spectrum of radar signals, and the BP neural network was used to train the power spectrum of different Gaussian windows, so as to improve the inversion accuracy. According to the study, the average error of soil moisture content inverted by AEA method was 0.45% in the range of 0-0.41m, while the error of ARMA method in depth range of 0.1-1.0m was less than 1%. The results showed that the combination of the two methods can effectively invert the soil moisture content within the radar detection range.

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Keywords: Ground penetrating radar, AEA, ARMA, soil moisture content, BP neural network

Y. Wang, H. Tian, M. Liu [references] [full-text] [DOI: 10.13164/re.2024.0387] [Download Citations]
EFU Net: Edge Information Fused 3D Unet for Brain Tumor Segmentation

Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part.

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Keywords: Deep learning, brain tumor segmentation, encoder decoder structure, edge attention mechanism, hybrid loss function

A. Zaib, A. R. Masood, M. A. Abdullah, S. Khattak, A. B. Saleem, I. Ullah [references] [full-text] [DOI: 10.13164/re.2024.0397] [Download Citations]
AESA Antennas using Machine Learning with Reduced Dataset

This paper proposes a deep neural network (DNN)-based approach for radiation pattern synthesis of 8 elements phased array antenna. For this purpose, 181 points of a desired radiation pattern are fed as input to the DNN and phases of array elements are extracted as the outputs. Existing DNN techniques for radiation pattern synthesis are not directly applicable to higher-order arrays as the dataset size grows exponentially with array dimensions. To overcome this bottleneck, we propose novel and efficient methods of generating datasets for DNN. Specifically, by leveraging the constant phase-shift characteristic of the phased array antenna, dataset size is reduced by several orders of magnitude and made independent of the array size. This has considerable advantages in terms of speed and complexity, especially in real-time applications as the DNN can immediately learn and synthesize the desired patterns. The performance of the proposed methods is validated by using an ideal square beam and an optimal array pattern as reference inputs to the DNN. The results generated in MATLAB as well as in CST, demonstrate the effectiveness of the proposed methods in synthesizing the desired radiation patterns.

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Keywords: Machine learning, neural networks, deep neural networks, active electronically scanned array, phased array, array pattern, Computer Simulation Technology

L. Xu, P. Tong, Y. Wei [references] [full-text] [DOI: 10.13164/re.2024.0406] [Download Citations]
Target Tracking with Variational Multi-Detection Mode under Unknown Parameters for HFHSSWR

The shipborne High-Frequency Hybrid Sky-Surface Wave Radar integrates a sky-wave transmitting channel and a ground-wave receiving channel on a shipborne platform. This hybrid radar system combines a skywave source with the added flexibility of a far-away shipborne radar. Ionospheric stratification and height uncertainty introduce uncertainties in the sky-wave channel, resulting in multiple measurements of one target. Additionally, the shipborne platform position is affected by sea state, causing errors in azimuth accuracy setting and subsequently reducing target tracking precision. In this paper, we propose for the first time a target tracking method that combines ionospheric variations with the motion of a shipborne platform. It introduces the variational Bayesian method into the multiple detection mode, which solves the effects of ionospheric altitude error and orientation error of shipborne platforms due to different sea states on target tracking. Simulation experiments validate the effectiveness of the proposed method. Therefore, the proposed method promises advancements in shipborne radar systems for maritime surveillance applications.

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Keywords: Shipborne HFHSSWR, target tracking methods, ionospheric disturbance, unknown parameters

L. Hu, Y. Shao, Y. Qian, F. Du, J. Li, Y. Lin, Z. Wang [references] [full-text] [DOI: 10.13164/re.2024.0417] [Download Citations]
Meta-Reinforcement Learning in Time-Varying UAV Communications: Adaptive Anti-Jamming Channel Selection

Unmanned Aerial Vehicle (UAV) communication networks are vulnerable to malicious jamming and co-channel interference, deteriorating the performance of the networks. Therefore, the exploration of anti-jamming methods to enhance communication security becomes a significant challenge. In this paper, we propose a novel anti-jamming channel selection scheme in a multi-channel multi-UAV network. We first formulate the anti-jamming problem as a Partially Observable Stochastic Game (POSG), where the UAV pairs with partial observability compete for a limited number of communication channels against a Markov jammer. To ensure rapid adaptation to the dynamic jamming environment, we propose a Meta-Mean-Field Q-learning (MMFQ) algorithm, which provides a Nash Equilibrium (NE) solution to the POSG problem. Furthermore, we derive the expressions of the upper bound for the loss function of MMFQ and prove the convergence of the proposed algorithm. Simulation results demonstrate that the proposed algorithm can achieve a superior average reward compared to the benchmark algorithms, facilitating throughput enhancement and resource utilization increase, especially for large-scale UAV communication networks.

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Keywords: Unmanned aerial vehicle (UAV) communication, anti-jamming, meta-reinforcement learning, mean field

O. Zeleny, T. Fryza, T. Bravenec, S. Azizi, G. Nair [references] [full-text] [DOI: 10.13164/re.2024.0432] [Download Citations]
Detection of Room Occupancy in Smart Buildings

Recent advancements in occupancy and indoor environmental monitoring have encouraged the development of innovative solutions. This paper presents a novel approach to room occupancy detection using Wi-Fi probe requests and machine learning techniques. We propose a methodology that splits occupancy detection into two distinct subtasks: personnel presence detection, where the model predicts whether someone is present in the room, and occupancy level detection, which estimates the number of occupants on a six-level scale (ranging from 1 person to up to 25 people) based on probe requests. To achieve this, we evaluated three types of neural networks: CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). Our experimental results show that the GRU model exhibits superior performance in both tasks. For personnel presence detection, the GRU model achieves an accuracy of 91.8%, outperforming the CNN and LSTM models with accuracies of 88.7% and 63.8%, respectively. This demonstrates the effectiveness of GRU in discerning room occupancy. Furthermore, for occupancy level detection, the GRU model achieves an accuracy of 75.1%, surpassing the CNN and LSTM models with accuracies of 47.1% and 52.8%, respectively. This research contributes to the field of occupancy detection by providing a cost-effective solution that utilizes existing Wi-Fi infrastructure and demonstrates the potential of machine learning techniques in accurately classifying room occupancy.

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Keywords: Occupancy detection, probe requests, Wi-Fi, energy savings, machine learning

M. Y. Onay [references] [full-text] [DOI: 10.13164/re.2024.0442] [Download Citations]
Dynamic Time Allocation Based Physical Layer Security for Jammer-Aided Symbiotic Radio Networks

Symbiotic Radio Networks (SRNs) have emerged as an important communication protocol to solve the increasing energy demand and spectrum resource shortage. However, the low bit rates of the devices working in SRNs during backscatter communication, where the surrounding radio frequency resources are used by subsystems different from the main system, make SRNs very vulnerable to external attacks such as eavesdropping and jamming. To solve this problem, the Physical Layer Security (PLS) for SRNs with Signal Emitter (SE), user, jammer, receiver and eavesdropper (ED) is analyzed. While the SE conveys its information to the receiver, the user assists the SE in part of the time period and transmits its information to the receiver in the other part. While ED is overhearing SE and user's information over the wiretap channel, the jammer is trying to prevent ED with the signal it emits. This model, in which the secrecy rate is maximized over time parameters, is the first approach in which PLS analysis is carried out in the presence of a cooperative jammer when the perfect/imperfect Successive Interference Cancellation (SIC) technique is used at the receiver. Numerical results show that the existence of a symbiotic relationship between the user and the SE increases the secrecy rate of the system compared to the non-symbiotic situation. Moreover, adopting the perfect SIC technique at the receiver without energy constraint at the user resulted in a significant increase in PLS performance compared to the imperfect SIC under energy constraint.

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Keywords: Symbiotic radio networks, sixth generation (6G), physical layer security, eavesdropper, jammer, imperfect successive interference cancellation

C. L. Ma, M. X. He, X. Wang, P. Cui, W. Wang, Y. D. Cao, X. C. Gao, L. Bai, D. Z. Bi [references] [full-text] [DOI: 10.13164/re.2024.0452] [Download Citations]
The Research of Ultra-Low Delay Gateway for Under-ground Remote Control

The existing systems for coal mine safety oversight, worker location tracking, and video surveillance face issues such as intricate network topologies, limited capacity for additional device connectivity, and significant latency. Aiming at the complex underground multi-source heterogeneous network, this paper compares and analyzes the defects and shortcomings of the current network, designs the time-sharing protocol mapping model based on MII (Media Independent Interface), SPI (Serial Peripheral Interface), DMA (Direct Memory Access) and other high-speed data exchange bus, and proposes a fusion gateway suitable for underground ultra-low delay. The actual operation results show that the gateway has the characteristics of rich interface, less packet loss and low delay, which can meet the real-time communication requirements of underground CAN (Controller Area Network) bus, IoT (Internet of Things) and Ethernet equipment. This paper is part of special issue AI-DRIVEN SECURE COMMUNICATION IN MASSIVE IOT FOR 5G AND BEYOND.

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Keywords: Integration gateway, ultra-low delay, high speed bus, coal mine gateway, time delay test