Radio Frequency Signal Research Articles

UAV Detection Using Reinforcement Learning.

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.

On the extraction of RF fingerprints from LSTM hidden-state values for robust open-set detection

New capabilities in wireless network security have been enabled by deep learning that leverages and exploits signal patterns and characteristics in Radio Frequency (RF) data captured by radio receivers to identify and authenticate radio transmitters. Open-set detection is an area of deep learning that aims to identify RF data samples captured from new devices during deployment (aka inference) that were not part of the training set; i.e. devices that were unseen during training. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. In this paper, we introduce a novel open-set detection approach for RF data-driven device identification that extracts its neural network features from patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory (CNN+LSTM) model. Experimental results obtained using real datasets collected from 15 IoT devices, each enabled with LoRa, wireless-Wi-Fi, and wired-Wi-Fi communication protocols, show that our new approach greatly improves the area under the precision-recall curve, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.

A complete artificial intelligence pipeline for radio frequency energy prediction in cellular bands for energy harvesting systems

Radio Frequency (RF) energy harvesting has been used to power wireless and low-powered devices. However, RF energy harvesting has limitations in terms of the amount of power that can be collected based on signal availability. Hence, energy prediction is essential to improve energy harvesting circuits' performance. Previous research has mainly focused on improving power harvesting policies or theoretically estimating the harvested energy. Very few works have considered the prediction of the RF signal as time series data using real RF measurements. Moreover, challenges such as the power consumed by the circuit's harvesting decisions and the impact of outliers on the model performance haven't been addressed yet. This paper presents a complete pipeline for developing the best predictive model for RF energy in cellular frequency bands. Real-time measurements are taken in different frequency bands using software-defined radio technology. We use four artificial intelligence techniques to model the RF energy signal. Additionally, we propose an optimized model with an enhanced loss function, which makes the model more resilient to anomalies, saving computational power and time consumed in cleaning the data. The four algorithms are investigated, and their prediction accuracies are compared. The average power of a period of 5 min is accurately forecasted. Numerical results in the 1960 MHz band show that long short-term memory has the best performance, followed by the DeepAR algorithm with prediction accuracies of 95.76% and 95.02%, respectively. Moreover, the proposed optimized model showed a 32.2% lower prediction error than the traditional models.

Digital twin of multi-model drone detection system on Airsim for RF and vision modalities

Drones have become more prevalent in recent years and are used for both beneficial and malicious purposes. As a result, protecting restricted areas from unauthorized drone activities has become crucial. However, some researchers face challenges in developing drone detection systems due to the high costs of necessary equipment. This paper presents an innovative solution by creating an Airsim Graphical User Interface (GUI) tool compatible with the Unreal Engine. This tool enables the simulation of drone flights and creation of image and radio frequency (RF) datasets for drone detection in a simulation environment. Our approach involves modeling the measurement devices such as cameras to capture image data and software defined radio (SDR) receiver to capture RF signals as raw in-phase and quadrature (IQ) data. Moreover, users can manage automated route planning for drones, recording configurations, and different cameras and RF configurations. Researchers can now generate datasets with various images and RF configurations without the need for physical drones, cameras, or SDRs, enabling experimentation with different drone detection models. Furthermore, we proposed models for drone detection systems by using generated datasets from the proposed dataset generation system.

Coherent multi-band MIMO radar: robustness analysis to SSMF-based RF signal delivery.

A numerical evaluation is conducted to assess the impact of distributing radio frequency (RF) signals through optical fiber links on the performance of a coherent multi-band multiple-input multiple-output (MIMO) radar system. The analysis focuses on scenarios where the antennas are widely separated in comparison to the employed signal wavelengths. The development of a model to quantify the phase noise (PN) induced on each RF band due to the signal transmission through optical fiber links between the centralized base station and each radar peripheral is described. Monte Carlo simulation results are collected to estimate the key performance indicators (KPIs) for varying standard single-mode fiber (SSMF) length and different PN contributions. The main contributors to the PN are revealed to be chromatic dispersion (CD), double Rayleigh scattering (DRS), and mechanical vibrations. In a shipborne scenario, a significant performance degradation occurs only when the length of the fiber links reaches approximately 20 km. Further, the PN impact has also been studied in a shipborne scenario to analyze the robustness of the system for worse phase noise level assumptions. The results reveal excellent robustness of the proposed centralized acquisition and processing approach in the presence of both very long fiber links and economically employed RF oscillators.

A compact all-frequency absorptive dual-band bandpass filter with T-shaped transmission line absorption branches

Multi-band and all-band absorptive filters can greatly promote the miniaturization of radio frequency (RF) communication systems. However, the existing absorptive multi-band filters have problems such as large size, narrow passband, high insertion loss, and low RF signal absorption capacity. Herein, a new all-frequency absorptive dual-band bandpass filter (BPF) is proposed by combining an N-shaped microstrip coupler with T-shaped transmission line absorption branches. The N-shaped microstrip coupler can flexibly implement the dual-band bandpass filtering of RF radio frequency (RF) signals in a compact structure. The T-shaped transmission lines can effectively absorb the stopband signals reflected from the N-shaped coupler at full frequency. The theoretical and experimental results show that the 3-dB fractional bandwidths (FBWs) of the proposed BPF with a size of 0.41 λg × 0.20 λg are 60.5 % at 1.3 GHz and 13.3 % at 4.1 GHz, respectively, and the return losses in all frequency are greater than 10 dB, indicating that the filter has the characteristics of wide passband, small size, and all-frequency absorption of reflected bandstop signals. In short, the combination of N-shaped coupler and T-shaped transmission line provides a way to achieve an all-frequency absorptive dual-band BPF with a small size, wide passband and low return loss.

Interference suppression and low complexity communication reception for OOK-chirp based RCC system

The existing radar-communication coexistence (RCC) waveform design based on chirp signal causes the increase of the complexity in the communication receiver, and multi-dimensional reuse also introduces more interference, leading to the deterioration of dual function performance. In this paper, a frequency division RCC system with on-off keying (OOK) based on frequency modulated continuous wave is designed for near sensors wake-up or low rate broadcast. The RCC signal has the fixed whole band and the variable sub-bands for radar signal and communication signal. We propose a two-stage envelope detection (TSED) scheme by extracting data from the frequency difference component of the two signals at the receiver without generating local radio frequency signal or using a mixer. By flexibly regulating the frequency band of two signals, the anti-interference ability of TSED can be further improved. In radar system, we perform small frequency hopping (FH) on radar signal to realize the full use of the whole band, and propose a scheme of abnormal peak detection and elimination (APDE) to eliminate the chirp interference. The simulation results show that the signal-to-interference ratio of TSED scheme for the RCC waveform can be respectively enhanced by 6 dB and 2 dB if compared with that of conventional envelope detection and coherent demodulation with a bit error rate of 10−5. Besides, The APDE scheme based on small FH reduces the range sidelobe by 20 dB and completely eliminates the added in-band interference.

Ultrafast electro-optic time-frequency fractional Fourier imaging at the single-photon level.

The Fractional Fourier Transform (FRT) corresponds to an arbitrary-angle rotation in the phase space, e.g., the time-frequency (TF) space, and generalizes the fundamentally important Fourier Transform. FRT applications range from classical signal processing (e.g., time-correlated noise optimal filtering) to emerging quantum technologies (e.g., super-resolution TF sensing) which rely on or benefit from coherent low-noise TF operations. Here a versatile low-noise single-photon-compatible implementation of the FRT is presented. Optical TF FRT can be synthesized as a series of a spectral disperser, a time-lens, and another spectral disperser. Relying on the state-of-the-art electro-optic modulators (EOM) for the time-lens, our method avoids added noise inherent to the alternatives based on non-linear optical interactions (such as wave-mixing, cross-phase modulation, or parametric processes). Precise control of the EOM-driving radio-frequency signal enables fast all-electronic control of the FRT angle. In the experiment, we demonstrate FRT angles of up to 1.63 rad for pairs of coherent temporally separated 11.5 ps-wide pulses in the near-infrared (800 nm). We observe a good agreement between the simulated and measured output spectra in the bright-light and single-photon-level regimes, and for a range of pulse separations (20 ps to 26.7 ps). Furthermore, a tradeoff is established between the maximal FRT angle and optical bandwidth, with the current setup accommodating up to 248 GHz of bandwidth. With the ongoing progress in EOM on-chip integration, we envisage excellent scalability and vast applications in all-optical TF processing both in the classical and quantum regimes.

A Reconfigurable Local Oscillator Harmonic Mixer with Simultaneous Phase Shifting and Image Rejection

A Reconfigurable Local Oscillator Harmonic Mixer with Simultaneous Phase Shifting and Image Rejection

Use of the 433MHz band for underwater communications in controlled environments

This research aims to design and implement an underwater communication system using radio frequency modules and Arduino. Underwater wireless communication is challenging due to the attenuation of electromagnetic waves in water. However, radio frequency signals in the 433MHz range and the added antenna have shown successful data transfer. For this purpose, measurements are made at different distances, and data is collected in the aquatic environment and free space. A comparative analysis of the values of arrival powers and losses in the studied environment is carried out to verify the efficiency of this study in terms of range, transmission speed, and reliability.

All-optical, filter-free, image-reject, and fundamental/sub-harmonic downconverted RoF link

An all-optical, image-reject, and fundamental/sub-harmonic downconverted radio-over-fiber (RoF) link using a polarization-division multiplexing Mach–Zehnder modulator (PDM-MZM) and fiber-dispersion effect is proposed. At the remote antenna unit, the receiving radio frequency (RF) signal and the local oscillator (LO) signal are used to drive two sub-MZMs of the PDM-MZM. After a length of optical fiber transmission, at the central office, by using the fiber-dispersion effect and controlling the polarization controller to introduce an appropriate phase shift between the RF and LO sidebands, image rejection is achieved and power fading is avoided simultaneously. Because the scheme is a simple and all-optical system without electrical frequency-dependent devices or filters, it has a good image-reject capability over a large working frequency range. Besides, the downconverter in sub-harmonic mode can decrease frequency requirement of the LO signal. Experimental results indicate that in the fundamental and sub-harmonic downconverters, for a single-tone signal, IRRs are over 47 dB. Image rejection for the 100-Mbaud 16-QAM signal and demodulated desired IF signal with a good EVM of less than 7% are realized. The proposed structure can be applied in RoF systems and microwave and millimeter-wave receivers.

Reconstruction methods for super-resolution imaging with PSF modulation

Recently, a super-resolution imaging method called the PSF (point spread function) modulation method was developed (Lu, IEEE TUFFC 2024). In this method, the amplitude, phase, or both of the PSF of a linear shift-invariant (LSI) imaging system is modulated so that the modulated PSF has a higher spatial frequency than that of the original PSF to reconstruct super-resolution images. The modulator can be produced and manipulated remotely by methods such as radiation force or it can be a physical particle such as micro- or nano-particle manipulated by an external force such as electrical and electromagnetic force. In principle, the super-resolution imaging method can be applied to any LSI imaging system, such as ultrasound, optical, photoacoustic, electromagnetic, underwater, nondestructive evaluation (NDE), and magnetic resonance imaging (MRI) system. These include pulse-echo ultrasound imaging, transmission imaging, wave source/field imaging, acoustical camera, and optical bright-field microscope. To optimize the quality of the images, methods for the reconstruction of pulse-echo and wave source/field super-resolution images are studied and the results will be presented. These methods include the uses of an analytic envelope of radio-frequency (RF) signals with and without windowing, “I” (in-phase) and “Q” (quadrature) signals, and the DC (direct current) component removal.

Vector-based feedback of continuous wave radiofrequency compression cavity for ultrafast electron diffraction.

The temporal resolution of ultrafast electron diffraction at weakly relativistic beam energies (100 keV) suffers from space-charge induced electron pulse broadening. We describe the implementation of a radio frequency (RF) cavity operating in the continuous wave regime to compress high repetition rate electron bunches from a 40.4 kV DC photoinjector for ultrafast electron diffraction applications. Active stabilization of the RF amplitude and phase through a feedback loop based on the demodulated in-phase and quadrature components of the RF signal is demonstrated. This scheme yields 144 ± 19 fs RMS temporal resolution in pump-probe studies.

A programmable solar‐powered radio frequency transmitter

AbstractThis paper proposes a programmable solar‐powered radio frequency (RF) transmitter. It employs a novel five‐layer structure that effectively integrates solar cells, active circuits, and broadband antenna. This design can directly emit RF signals without the need for additional power supply, enabling efficient energy conversion and transmission. In addition, a U‐shaped coupling line model is introduced to evaluate the performance of the active circuit. We fabricated a sample and measured it. The results of the measurements indicate that the proposed RF transmitter can achieve self‐powering and programmable output at any frequency within the range of 2.08–4.1 GHz, with an output power of no less than 14 dBm. Moreover, the solar cells are placed on both sides of the circuits and antenna for energy harvesting, achieving 100% sunlight insolation with a 75.5% coverage ratio. This design can be used for self‐powered green wireless system applications.

Environmental causality calibration: Advancing WLAN RF fingerprinting for precise indoor localization.

In recent years, considerable and valuable research progress has been made in indoor positioning technologies based on WLAN Radio Frequency (RF) fingerprinting, identifying it as one of the most promising positioning technologies with substantial potential for wider adoption. However, indoor environmental factors significantly influence the propagation of wireless RF signals, resulting in a considerable decrease in positioning accuracy as the indoor environmental conditions vary. Thus, effectively mitigating the impact of indoor environmental factors on WLAN RF fingerprinting-based positioning systems has become a crucial research problem. Currently, there is a dearth of comprehensive research on the influence of indoor climatic factors, particularly the variations in relative humidity, on the propagation of WLAN RF signals within indoor spaces and its consequential impact on positioning accuracy. To address the aforementioned issues, this paper proposes an Adaptive expansion fingerprint database (AeFd) model based on a regression learning algorithm. The AeFd, through the design of a relationship model describing the interaction between fingerprint databases under varying relative humidity, allows the fingerprint database expanded by AeFd to dynamically adapt to the changes in indoor relative humidity. Our experiments show that using the AeFd model with the KNN algorithm, a 5% performance improvement was observed over 10 days and an 8% improvement over 10 months. According to experimental test results, the fingerprint database expansion model AeFd proposed in this paper can effectively expand the fingerprint database under different relative humidity levels, thereby significantly enhancing the positioning performance of the system and improving its stability.

A 20-GHz On-Chip Six-Port Reflectometer Using Simple Lumped Passive Devices and Bipolar Junction Transistors

This paper proposes an on-chip six-port reflectometer (SPR) fabricated in the 0.13-μm IBM BiCMOS-8HP technology. The SPR enjoys a compact circuit structure, with only four amplitude detectors as active devices, one resistive power divider, and one lumped phase shifter as passive devices. The power divider and phase shifter are responsible for manipulating the radio-frequency (RF) signals appropriately, whereas the detectors are responsible for sensing the processed signals. The chip area, which can be further reduced, is 1.25 mm in width and 1 mm in height. The SPR can perform in-situ measurement of reflection coefficients of devices under test (DUTs) and reduce testing costs of RF chips by using vector network analyzers (VNAs). The SPR demonstrates excellent performance in measuring the reflection coefficients of DUTs at around 20 GHz. The experimental results indicate that the maximum error of the measured reflection coefficients in absolute value is about -26 dB.

Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence.

The proliferation of radio frequency (RF) devices in contemporary society, especially in the fields of smart homes, Internet of Things (IoT) gadgets, and smartphones, underscores the urgent need for robust identification methods to strengthen cybersecurity. This paper delves into the realms of RF fingerprint (RFF) based on applying the Jensen-Shannon divergence (JSD) to the statistical distribution of noise in RF signals to identify Bluetooth devices. Thus, through a detailed case study, Bluetooth RF noise taken at 5 Gsps from different devices is explored. A noise model is considered to extract a unique, universal, permanent, permanent, collectable, and robust statistical RFF that identifies each Bluetooth device. Then, the different JSD noise signals provided by Bluetooth devices are contrasted with the statistical RFF of all devices and a membership resolution is declared. The study shows that this way of identifying Bluetooth devices based on RFF allows one to discern between devices of the same make and model, achieving 99.5% identification effectiveness. By leveraging statistical RFFs extracted from noise in RF signals emitted by devices, this research not only contributes to the advancement of the field of implicit device authentication systems based on wireless communication but also provides valuable insights into the practical implementation of RF identification techniques, which could be useful in forensic processes.

Dual vector millimeter-wave signal generation based on optical carrier suppression modulation and direct detection with one photodetector.

We propose a novel, to the best of our knowledge, scheme for dual vector millimeter-wave (mm-wave) signal generation and transmission, based on optical carrier suppression (OCS) modulation, precoding, and direct detection by a single-ended photodiode (PD). At the transmitter side, two independent vector radio frequency (RF) signals with precoding, generated via digital signal processing (DSP), are used to drive an in-phase/quadrature (I/Q) modulator operating at the optical OCS modulation mode to simultaneously generate two independent frequency-doubling optical vector mm-wave signals, which can reduce the bandwidth requirement of transmitter's components and enhance spectral efficiency. With the aid of the single-ended PD and subsequent DSP at the receiver side, two independent frequency-doubling vector mm-wave signals can be separated and demodulated without data error. Based on our proposed scheme, we experimentally demonstrate the generation, transmission, and detection of 2-Gbaud 30-GHz quadrature-phase-shift-keying (QPSK) and 2-Gbaud 46-GHz QPSK signals over 10-km single-mode fiber-28 (SMF-28) and 1-m wireless transmission. The results indicate that the bit-error ratio (BER) of the dual vector mm-wave signals can each reach the hard-decision forward-error-correction (HD-FEC) threshold of 3.8 × 10-3.

Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

Analog feature extraction (AFE) is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data. However, applying AFE to broadband radio-frequency (RF) scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry. Here, we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain. The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas, extracting valid features from both temporal and spatial dimensions. Because of the tunability of the photonic devices, the photonic spatiotemporal feature extractor is trainable, which enhances the validity of the extracted features. Moreover, a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor. To validate our scheme, we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth. Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%. Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing, with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving, robotics, and smart factories.

Performance Analysis for Reconfigurable Intelligent Surface-aided Communication Systems with Energy Harvesting under Imperfect Nakagami-m Channel Information

Reconfigurable intelligent surface (RIS) can serve as a passive relay to maintain communication between a transceiver in severe scenarios of no direct link between them. In addition, harvesting energy from radio frequency (RF) signals can meliorate significantly energy efficiency. In this research, we propose RIS-aided communication systems with energy harvesting (RISwEH) which combine both RIS and RF energy harvesting to improve energy efficiency as well as communication reliability. To evaluate realistically and quickly the performance of the RISwEH, we propose the explicit formulas of the system throughput and the outage probability under the realistic scenario of Nakagami-m fading and imperfect channel state information (CSI). Moreover, we propose an optimization algorithm relied upon a Golden section search to attain the optimum value of the time splitting factor of energy harvester to obtain the best system performance. Various results corroborate the theoretical derivations, confirm the efficacy of the proposed optimization algorithm, and illustrate the influence of innumerable system settings on the system performance. Particularly, the imperfect CSI deteriorates considerably the system performance. Nonetheless, the performance of the RISwEH can be enhanced by accreting the quantity of the elements of the RIS as well as with the lower fading severity. Furthermore, the time splitting factor also impacts dramatically the outage performance of the RISwEH and its optimal value mitigates significantly the outage probability.