2023/2

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Radar-Based Human Motion Recognition by Using Vital Signs with ECA-CNN
(Společnost pro radioelektronické inženýrství, 2023-06) Chen, K.; Gu, M.; Chen, Z.
Radar technologies reserve a large latent capacity in dealing with human motion recognition (HMR). For the problem that it is challenging to quickly and accurately classify various complex motions, an HMR algorithm combing the attention mechanism and convolution neural network (ECA-CNN) using vital signs is proposed. Firstly, the original radar signal is obtained from human chest wall displacement. Chirp-Z Transform (CZT) algorithm is adopted to refine and amplify the narrow band spectrum region of interest in the global spectrum of the signal, and accurate information on the specific band is extracted. Secondly, six time-domain features were extracted for the neural network. Finally, an ECA-CNN is designed to improve classification accuracy, with a small size, fast speed, and high accuracy of 98%. This method can improve the classification accuracy and efficiency of the network to a large extent. Besides, the size of this network is 100 kb, which is convenient to integrate into the embedded devices.
Optimization of the Crest Factor for Complex-Valued Multisine Signals
(Společnost pro radioelektronické inženýrství, 2023-06) Cseppento, B.; Retzler, A.; Kollar, Z.
Multisine signals are commonly used in the measurement of dynamic systems and wireless channels. For optimal measurements with a high dynamic range, a low Crest Factor (CF) excitation signal is required. In this paper, a modified approach to optimize the crest factor for complex-valued multisine signals is presented. The approach uses a nonlinear optimization method where the real and imaginary parts can also be optimized for low CF. Furthermore, extensions of the real-valued multisine CF optimization methods are presented for complex-valued cases. The proposed methods are validated and compared using simulations. Based on the results it is shown that the novel approach can lead to more optimal signal design and lower CF compared to other techniques for complex-valued multisine signals.
Machine Learning Based Classification of IoT Traffic
(Společnost pro radioelektronické inženýrství, 2023-06) Velichkovska, B.; Cholakoska, A.; Atanasovski, V.
With the rapid expansion and widespread adoption of the Internet of Things (IoT), maintaining secure connections among active devices can be challenging. Since IoT devices are limited in power and storage, they cannot perform complex tasks, which makes them vulnerable to different types of attacks. Given the volume of data generated daily, detecting anomalous behavior can be demanding. However, machine learning (ML) algorithms have proven successful in extracting complex patterns from big data, which has led to active applications in IoT. In this paper, we perform a comprehensive analysis, including 4 ML algorithms and 3 neural networks (NNs), and propose a pipeline which analyzes the influence data reduction (loss) has on the performance of these algorithms. We use random undersampling as a data reduction technique, which simulates reduced network traffic data. The pipeline investigates several degrees of data loss. The results show that models trained on the original data distribution obtain accuracy that verges on 100%. XGBoost performs best from the classic ML algorithms. From the deep learning models, the 2-layered NN provides excellent results and has sufficient depth for practical application. On the other hand, when the models are trained on the undersampled data, there is a decrease in performance, most notably in the case of NNs. The most prominent change is seen in the 4-layered NN, where the model trained on the original dataset detects attacks with a success of 93.53%, whereas the model trained on the maximally reduced data has a success of only 39.39%.
SIW-Based Frequency-Tunable Self-Oscillating Active Integrated Antenna
(Společnost pro radioelektronické inženýrství, 2023-06) Zhang, D.; Chen, X.; Qi, S.; Zhang, H.
A frequency-tunable self-oscillating active integrated antenna (AIA) mainly composed of active circuit and 1×2 substrate integrated waveguide (SIW) antenna array is proposed in this paper. Manipulating bias voltage to the varactors loaded on SIW antenna could offer electronic control of oscillation frequency. The DC bias circuit of the varactors integrated in SIW cavity can provide compact structure. Due to the load effect of the high Q SIW cavity, the designed antenna exhibits low phase noise. According to the measured results, the effective isotropic radiated power (EIRP) ranges from 4.4 to 12.9 dBm which is superior to previous reports with the frequency tuning range of about 20 MHz. The phase noise is -92.7 dBc/Hz at 100 kHz offset. The measured results also show that the cross-polarization levels are almost 20 dB lower than the co-polarized one in the main beam direction at 5.698 GHz.
Sparsity Adaptive Compressive Sensing based Two-stage Channel Estimation Algorithm for Massive MIMO-OFDM Systems
(Společnost pro radioelektronické inženýrství, 2023-06) Ge, L. J.; Wang, Z. C.; Qian, L.; Wei, P.
Massive multi-input multioutput (MIMO) coupled with orthogonal frequency division multiplexing (OFDM) has been utilized extensively in wireless communication systems to investigate spatial diversity. However, the increasing need for channel estimate pilots greatly increases spectrum consumption and signal overhead in massive MIMO-OFDM systems. This paper proposes a two-stage channel estimation algorithm based on sparsity adaptive compressive sensing (CS) to address this issue. To estimate the channel state information (CSI) for pilot locations in Stage 1, we provide a geometry mean-based block orthogonal matching pursuit (GBMP) method. By calculating the geometric mean of the energy in the support set of the channel response, the GBMP method, when compared to conventional CS methods, can drastically reduce the number of iterations and effectively increase the convergence rate of channel reconstruction. Stage 2 involves estimating the CSI for nonpilot locations using a time-frequency correlation interpolation method, which can increase the accuracy of the channel estimation and is dependent on the estimated results from Stage 1. According to the simulation results, the proposed two-stage channel estimation algorithm greatly reduces the running time with little error performance degradation when compared to traditional channel estimating algorithms.

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