Radio Frequency Signal Research Articles

TFSemantic: A Time–Frequency Semantic GAN Framework for Imbalanced Classification Using Radio Signals

Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this article, we propose a time–frequency semantic generative adversarial network framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution and attention semantic feature embedding methods for the semantic extraction module. A discrete wavelet transform is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.

Development of 2400-2450 MHz Frequency Band RF Energy Harvesting System for Low-Power Device Operation.

Recently, there has been an increasing fascination for employing radio frequency (RF) energy harvesting techniques to energize various low-power devices by harnessing the ambient RF energy in the surroundings. This work outlines a novel advancement in RF energy harvesting (RFEH) technology, intending to power portable gadgets with minimal operating power demands. A high-gain receiver microstrip patch antenna was designed and tested to capture ambient RF residue, operating at 2450 MHz. Similarly, a two-stage Dickson voltage booster was developed and employed with the RFEH to transform the received RF signals into useful DC voltage signals. Additionally, an LC series circuit was utilized to ensure impedance matching between the antenna and rectifier, facilitating the extraction of maximum power from the developed prototype. The findings indicate that the developed rectifier attained a peak power conversion efficiency (PCE) of 64% when operating at an input power level of 0 dBm. During experimentation, the voltage booster demonstrated its capability to rectify a minimum input AC signal of only 50 mV, yielding a corresponding 180 mV output DC signal. Moreover, the maximum power of 4.60 µW was achieved when subjected to an input AC signal of 1500 mV with a load resistance of 470 kΩ. Finally, the devised RFEH was also tested in an open environment, receiving signals from Wi-Fi modems positioned at varying distances for evaluation.

Active interference stations based on radiophoton technology

Problem statement. A modern active interference station (SAP) is built on the basis of digital radio frequency memory (RFR). The CRCP technology allows storing copies of radio frequency signals in digital form, as well as performing their digital processing in order to generate signals with modulation of various types [1]. This technology allows you to create a SAP with the possibility of generating simulated interference (or digital copies of the target). SAP based on this technology has one significant drawback, the band of instant signal processing is limited by the capabilities of an analog-to-digital converter and, in terms of use in electronic warfare equipment, in most cases, does not satisfy the condition - the processing band is equal to the range of input signals [2]. The elimination of this disadvantage is possible in several ways, by increasing the number of channels in proportion to the input signal band to the instantaneous RFP band, by expanding the signal processing band using processing in Nyquist zones. Both ways are not enough effect

Estimation and Evaluation of Ionospheric Scintillations at Various NavIC Signal Frequencies using Real Time Data

The ionospheric scintillations affect radio waves from the NavIC satellites through the ionosphere. The ionospheric scintillations lower receiver tracking accuracy, integrity, and continuity. This paper presents the computation of ionosphere parameters such as TEC, ROTI and scintillation index (S) using pseudo range and Carrier to Noise density ratio (C/No) measurements of NavIC L5 and S-band signals. Also, investigates the ionospheric scintillations, which are common in the equatorial and low latitude region. The correlation between the amplitude scintillation index S4 and ROTI is investigated using real data of NavIC receiver at Osmania University, Hyderabad. The results obtained confirm that the scintillations impact varies with radio signal frequency and it is severe on low frequencies during post sunset hours at low latitude regions.

Power Spectral Characteristics of In‐Situ Irregularities and Topside GPS Signal Intensity at Low Latitudes Using High‐Sample‐Rate Swarm Echo (e‐POP) Measurements

AbstractIonospheric density structures at low latitudes range in size from thousands of kilometers down to a few meters. Radio frequency (RF) signals, such as those from global navigation satellite systems, that propagate through irregularities suffer from rapid fluctuations in phase and intensity, known as scintillations. In this study, we use the high‐sample‐rate measurements of the Swarm Echo (CASSIOPE/e‐POP) satellite's GPS Occultation (GAP‐O) receiver taken after its antenna was re‐oriented to vertical‐pointing, simultaneously with e‐POP Ion Mass Spectrometer surface current observations as a proxy for plasma density, to obtain the spectral characteristics of GPS signal intensity and in‐situ irregularities at altitudes from 350 to 1,280 km. We show that the power spectra of both measurements can generally be characterized by a power law. In the case of density irregularities, the spectral index with the highest occurrence rate is around 1.7, which is consistent with previous studies. Also, all the power spectra of GPS signal intensity in this study show a single spectral index near 2. Moreover, roll‐off frequencies estimated in this work range from 0.4 to 2.5 Hz, which is significantly higher than Fresnel frequencies calculated from ground GPS receivers at low latitudes (between 0.2 and 0.45 Hz). Part of this increase is due to the 8 km/s orbital velocity of Swarm Echo near perigee. Another key difference is that variations in the GPS signals in this study are dominated by the topside ionosphere, whereas GPS signals received from ground are affected mostly by the relatively dense F‐region plasma in the 250–350 km altitudinal range.

A high-performance surface acoustic wave sensing technique.

We present a superheterodyne-scheme demodulation system that can detect the amplitude and phase shift of weak radio frequency signals with extraordinarily high stability and resolution. As a demonstration, we introduce a process to measure the velocity of the surface acoustic wave using a delay-line device from 30K to room temperature, which can resolve <0.1 ppm velocity shift. Furthermore, we investigate the possibility of using this surface acoustic wave device as a calibration-free, high sensitivity, and fast response thermometer.

Photonic architecture for remote multi-parameter measurement and transmission of microwave signals

A photonic architecture for remote multi-parameter measurement and transmission of microwave signals is proposed and demonstrated, which utilizes a dual-parallel dual-drive Mach-Zehnder modulator (DP-DDMZM) in the antenna unit and a dual-drive Mach-Zehnder modulator (DDMZM) in the processing unit. Doppler frequency shift (DFS) and angle of arrival (AOA) can be determined by analyzing the down-converted intermediate frequency signals. Introducing a reference signal in the processing unit ensures DFS measurement without directional ambiguity. The proposed architecture can also be applied for instantaneous frequency measurement based on down-conversion. Due to the use of optical single sideband modulation, long-distance transmission of radio frequency (RF) signals without dispersion-induced power fading can be achieved. Experiments for accurate and stable DFS and AOA measurement as well as long-distance RF signal transmission with dispersion-induced power fading are presented. The approach avoids the use of optical filters and polarization-related devices, facilitating wideband and stable operation, which is highly desirable. The proposed architecture is a potential solution for microwave photonic antenna remoting, offering support for both remote transmission and multi-parameter measurement.

Ultra-low power, high-data rate, fully on-chip radio frequency on-off keying receiver for internet-of-things applications

Despite the enormous potential of energy-efficient receivers for wireless sensor networks, the large power consumption or limited data rate support impedes its extensive applications. Here, we present an energy-efficient, ultra-low power, higher data rate supporting, completely on-chip radio-frequency receiver frontend for on-off keying modulated signals in the 2.4 GHz industrial, scientific, and medical band. This compact-sized receiver is achieved by implementing temperature-resilient oscillator, pseudo-differential mixer, and a wideband detector while avoiding bulky external components such as bulk-acoustic wave resonators, crystal oscillators. Measurement results demonstrate that the proposed on-off keying receiver can decode low power level radio-frequency signals up to 5 Mbps data rate while consuming only 178 µW power. This work also demonstrates support for lower data rates at reduced power. Since the proposed receiver operates in different power modes, it can be integrated in diverse applications including internet-of-things devices and continuously monitoring biomedical/wearable implants.

Reconstruction of elasticity modulus distribution base on semi-supervised neural network

Accurate reconstruction of tissue elasticity modulus distribution has always been an important challenge in ultrasound elastography. Considering that existing deep learning-based supervised reconstruction methods only use simulated displacement data with random noise in training, which cannot fully provide the complexity and diversity brought by in-vivo ultrasound data, this study introduces the use of displacement data obtained by tracking in-vivo ultrasound radio frequency signals (i.e., real displacement data) during training, employing a semi-supervised approach to enhance the prediction accuracy of the model. Experimental results indicate that in phantom experiments, the semi-supervised model augmented with real displacement data provides more accurate predictions, with mean absolute errors and mean relative errors both around 3%, while the corresponding data for the fully supervised model are around 5%. When processing real displacement data, the area of prediction error of semi-supervised model was less than that of fully supervised model. The findings of this study confirm the effectiveness and practicality of the proposed approach, providing new insights for the application of deep learning methods in the reconstruction of elastic distribution from in-vivo ultrasound data.

Design and investigation of small-scale long-distance RF energy harvesting system for wireless charging using CNN, LSTM, and reinforcement learning

Introduction: The energy supply challenge in wireless charging applications is currently a significant research problem. To address this issue, this study introduces a novel small-scale long-distance radio frequency (RF) energy harvesting system that utilizes a hybrid model incorporating CNN, LSTM, and reinforcement learning. This research aims to improve RF energy harvesting and wireless charging efficiency.Method: The methodology of this study involves data collection, data processing, model training and evaluation, and integration of reinforcement learning algorithms. Firstly, RF signal data at different distances are collected and rigorously processed to create training and testing datasets. Next, the CNN-LSTM model is trained using the prepared data, and model performance is enhanced by adjusting hyperparameters. During the evaluation phase, specialized test data is used to assess the accuracy of the model in predicting RF energy harvesting and wireless charging efficiency. Finally, reinforcement learning algorithms are integrated, and a reward function is defined to incentivize efficient wireless charging and maximize energy harvesting, allowing the system to dynamically adjust its strategy in real time.Results: Experimental validation demonstrates that the optimized CNN-LSTM model exhibits high accuracy in predicting RF energy harvesting and wireless charging efficiency. Through the integration of reinforcement learning algorithms, the system can dynamically adjust its strategy in real time, maximizing energy harvesting efficiency and charging effectiveness. These results indicate significant progress in long-distance RF energy harvesting and wireless charging with this system.Discussion: The results of this study validate the outstanding performance of the small-scale long-distance RF energy harvesting system. This system is not only applicable to current wireless charging applications but also demonstrates potential in other wireless charging domains. Particularly, it holds significant prospects in providing energy support for wearable devices, Internet of Things (IoT), and mobile devices.

Sum rate maximization for RIS-assisted NOMA-enabled underlay AmBC-CR networks

Ambient backscatter communications (AmBC) have introduced a revolutionary approach to wireless communication that utilizes ambient radio frequency (RF) signals in contrast to self-generated RF signals. This makes AmBC a compelling candidate for future Internet-of-Things (IoT) applications, particularly considering the constraints of energy and spectrum resources. In this work, we investigate a cognitive radio (CR) based underlay network of backscatter devices (BDs) that communicate with the backscatter receiver (BR) by utilizing non-orthogonal multiple access (NOMA). Due to the dual fading effect in backscatter networks, the backscatter link is weakened which degrades the achievable performance. In order to tackle this issue, reconfigurable intelligent surface (RIS) is exploited in such networks. Therefore, to maximize the sum rate of the RIS-assisted NOMA-enabled underlay AmBC-CR (RIS-NUAC) network, subject to individual BDs’ data rate constraint and primary receiver (PR) interference threshold constraint, we jointly optimize the phase shift matrix (PSM) at the RIS and the power reflection coefficients (PRCs) at the BDs. To solve the resultant non-convex optimization problem, we propose a sub-optimal solution based on an alternating optimization (AO) algorithm by applying semidefinite relaxation (SDR) and rank one approximation. Based on simulation results, the proposed scheme demonstrates superior performance over baseline schemes, including NOMA with random phases, NOMA without RIS, orthogonal multiple access (OMA) with optimal phases, and OMA without RIS.

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction

Objective. Protoacoustic imaging showed great promise in providing real-time 3D dose verification of proton therapy. However, the limited acquisition angle in protoacoustic imaging induces severe artifacts, which impairs its accuracy for dose verification. In this study, we developed a hybrid-supervised deep learning method for protoacoustic imaging to address the limited view issue. Approach. We proposed a Recon-Enhance two-stage deep learning method. In the Recon-stage, a transformer-based network was developed to reconstruct initial pressure maps from raw acoustic signals. The network is trained in a hybrid-supervised approach, where it is first trained using supervision by the iteratively reconstructed pressure map and then fine-tuned using transfer learning and self-supervision based on the data fidelity constraint. In the enhance-stage, a 3D U-net is applied to further enhance the image quality with supervision from the ground truth pressure map. The final protoacoustic images are then converted to dose for proton verification. Main results. The results evaluated on a dataset of 126 prostate cancer patients achieved an average root mean squared errors (RMSE) of 0.0292, and an average structural similarity index measure (SSIM) of 0.9618, out-performing related start-of-the-art methods. Qualitative results also demonstrated that our approach addressed the limit-view issue with more details reconstructed. Dose verification achieved an average RMSE of 0.018, and an average SSIM of 0.9891. Gamma index evaluation demonstrated a high agreement (94.7% and 95.7% for 1%/3 mm and 1%/5 mm) between the predicted and the ground truth dose maps. Notably, the processing time was reduced to 6 s, demonstrating its feasibility for online 3D dose verification for prostate proton therapy. Significance. Our study achieved start-of-the-art performance in the challenging task of direct reconstruction from radiofrequency signals, demonstrating the great promise of PA imaging as a highly efficient and accurate tool for in vivo 3D proton dose verification to minimize the range uncertainties of proton therapy to improve its precision and outcomes.

Non Orthogonal Multiple Access (NOMA) Using a Nonlinear Energy Harvesting Model

This paper derives and optimizes the throughput of NOMA with nonlinear energy harvesting. The Base Station (BS) harvests energy from the received Radio Frequency signal. Then, the BS uses the nonlinear harvested power to diffuse packets to N users. We suggest to optimize harvesting time and the powers of users to optimize the data rate. The optimization of NOMA powers and the harvesting process were not yet performed with a non linear energy harvesting model. The study is performed for Rayleigh channels in the presence of nonlinear energy harvesting model. Furthermore, the theoretical results were confirmed using MATLAB computer simulations.

Entanglement between two charge qubits taking account the Kerr media

Background. The need to implement controlled coupling between qubits, which are the logical elements of quantum devices such as quantum computers and quantum networks, requires, along with the use of traditional methods, the development of new, more effective ways to organize the interaction of qubits with the microwave fields of resonators used to generate and control the entanglement of qubits. As one of these methods, a method based on the influence of frequency-regulated radio frequency signals on a superconducting Josephson qubit connected by a large Josephson junction to a free qubit has been proposed.&#x0D; Aim. The influence of the Kerr medium of the resonator, in which one of the two qubits is placed, on their entanglement induced by the coherent or thermal frequency-regulated radio frequency field of the resonator is considered.&#x0D; Methods. To analyze the dynamics of the system under consideration, the solution of the quantum Liouville equation for the full density matrix is studied. An exact solution o this equation is found in the case of initial separable and entangled states of qubits. The exact solution of the evolution equation is used to calculate the criterion of qubit-qubit entanglement – cconcurrence. Numerical modeling of the concurrence was carried out for various states of qubits, coherent and thermal fields of the resonator, as well as various values of the intensity of the resonator field and the Kerr nonlinearity parameter.&#x0D; Results. It is shown that for separable initial states of qubits, the inclusion of Kerr nonlinearity reduces the maximum degree of entanglement of qubits. For an entangled initial state of qubits, the possibility of creating long-lived entangled states in the presence of Kerr nonlinearity is shown.&#x0D; Conclusion. The type of initial states of qubits and the range of values of the intensities of the resonator fields and the Kerr nonlinearity parameters have been established, for which the most effective control and operation of the evolution of qubits, as well as the degree of their entanglement, in the physical system under consideration, is possible.

Securing Your Airspace: Detection of Drones Trespassing Protected Areas.

Unmanned Aerial Vehicle (UAV) deployment has risen rapidly in recent years. They are now used in a wide range of applications, from critical safety-of-life scenarios like nuclear power plant surveillance to entertainment and hobby applications. While the popularity of drones has grown lately, the associated intentional and unintentional security threats require adequate consideration. Thus, there is an urgent need for real-time accurate detection and classification of drones. This article provides an overview of drone detection approaches, highlighting their benefits and limitations. We analyze detection techniques that employ radars, acoustic and optical sensors, and emitted radio frequency (RF) signals. We compare their performance, accuracy, and cost under different operating conditions. We conclude that multi-sensor detection systems offer more compelling results, but further research is required.

MeshID: Few-Shot Finger Gesture Based User Identification Using Orthogonal Signal Interference.

Radio frequency (RF) technology has been applied to enable advanced behavioral sensing in human-computer interaction. Due to its device-free sensing capability and wide availability on Internet of Things devices. Enabling finger gesture-based identification with high accuracy can be challenging due to low RF signal resolution and user heterogeneity. In this paper, we propose MeshID, a novel RF-based user identification scheme that enables identification through finger gestures with high accuracy. MeshID significantly improves the sensing sensitivity on RF signal interference, and hence is able to extract subtle individual biometrics through velocity distribution profiling (VDP) features from less-distinct finger motions such as drawing digits in the air. We design an efficient few-shot model retraining framework based on first component reverse module, achieving high model robustness and performance in a complex environment. We conduct comprehensive real-world experiments and the results show that MeshID achieves a user identification accuracy of 95.17% on average in three indoor environments. The results indicate that MeshID outperforms the state-of-the-art in identification performance with less cost.

Real-time tracking of radial artery vessel wall using a Kalman filter-based ultrasound single-plane wave RF signal time-frequency information fusion algorithm

ObjectiveThe goal of our study was to achieve real-time dynamic tracking of the radial artery vessel wall in everyday life, which is crucial for blood pressure estimation and cardiovascular disease prediction. MethodsThe algorithm integrates time–frequency information from ultrasound single-plane wave RF (radiofrequency) signals and consists of three main components: feature frame ROI (region of interest) selection, inter-frame cross-correlation and intra-frame autocorrelation, and feature frame Kalman filtering. ResultsExperimental validation on a silicone gel ultrasound phantom with simulated blood vessels demonstrates an average diameter estimation error of less than 0.5% compared to ground truth values. Comparative experiments show similar relative errors (2.83%, 2.43%) to the optical flow method, with a computational speed 7.32 times faster. The algorithm accurately aligns extracted pulse waves with pressure pulse waves at six local feature points per cycle. ConclusionThe proposed method achieves a favorable balance between speed and accuracy in tracking the radial artery vessel wall. It holds significant potential for real-time monitoring of physiological health parameters, offering precise and dynamic tracking capabilities. SignificanceThis algorithm addresses the challenges of non-invasive real-time tracking, benefiting blood pressure estimation and cardiovascular disease prediction. Its integration of time–frequency information and efficient computational speed make it valuable for the scientific community and public in monitoring physiological health parameters.

Machine Learning for the Design and the Simulation of Radiofrequency Magnetic Resonance Coils: Literature Review, Challenges, and Perspectives.

Radiofrequency (RF) coils for magnetic resonance imaging (MRI) applications serve to generate RF fields to excite the nuclei in the sample (transmit coil) and to pick up the RF signals emitted by the nuclei (receive coil). For the purpose of optimizing the image quality, the performance of RF coils has to be maximized. In particular, the transmit coil has to provide a homogeneous RF magnetic field, while the receive coil has to provide the highest signal-to-noise ratio (SNR). Thus, particular attention must be paid to the coil simulation and design phases, which can be performed with different computer simulation techniques. Being largely used in many sectors of engineering and sciences, machine learning (ML) is a promising method among the different emerging strategies for coil simulation and design. Starting from the applications of ML algorithms in MRI and a short description of the RF coil's performance parameters, this narrative review describes the applications of such techniques for the simulation and design of RF coils for MRI, by including deep learning (DL) and ML-based algorithms for solving electromagnetic problems.

Drone detection with radio frequency signals and deep learning models

The widespread use of drones raises security, environmental, privacy, and ethical issues; therefore, effective detection by drones is important. There are several methods for detecting drones, such as wireless signal detection, photoelectric detection, radar detection, and sound detection. However, these detection methods are not accurate enough to identify drones for use. To solve this question, more robust drone detection method are needed. In addition, for different types of drones and application scenarios, different technical means need to be used for detection and identification. Based on 2-class ,4-class and 10-class problems on an open ratio frequency (RF) signal dataset, we compared the drone detection and classification performances of different machine learning with deep learning models and multi-task models which is proposed by combining different RF methods with Convolutional neural networks (CNNs). Our experimental results show that the XGBoost model achieved the latest results on this groundbreaking dataset, with 99.96% accuracy for 2-class problem, 92.31% accuracy for 4-class problem, and 74.81% accuracy for 10-class problem, which exhibits the best performance for drone detection and classification.

Non‐Hermitian Broadside Coupled Split Ring Resonators with Directional Sensitivity

AbstractDirectional radiofrequency (RF) sensing, also known as radio direction finding, is essential in various applications that involve localizing RF sources. However, existing RF sensing technologies face challenges due to their large antenna sizes and complex RF signal processing circuits, which hinder the realization of miniaturized subwavelength direction sensors. In this article, a non‐Hermitian broadside coupled split ring resonators (BC‐SRRs) are presented, which can identify the direction of an incident RF signal with angles ranging from 0° to 180°. The non‐Hermitian Hamiltonian of the system, which can be interpreted by the temporal coupled mode theory, gives rise to asymmetric resonant modes of the BC‐SRRs. The asymmetry allows the direct measurement of RF wave incident angle by probing the resonant strength of the two BC‐SRRs. For the proof of concept, a one‐stage Dickson voltage multiplier is employed to rectify the RF signals and demonstrate angle sensing ability by relating the rectified voltage to the incident angle. The results showcase the potential of the proposed technique using non‐Hermitian BC‐SRRs as a pathway toward subwavelength antenna‐based radio direction finding.