Search Results (37)

In recent years, radio signal classification has become a hot topic in the field of wireless communication. However, current algorithms have low classification accuracy at low signal-to-noise radio (SNR) signals, and under this condition, they cannot achieve good classification results of mixed radio signals either. In this paper, we proposed an adaptive model based on feedforward neural network (FNN), convolutional neural network (CNN), and Transformer, named FCT. FCT is proposed to achieve better classification performance on mixed radio signals by leveraging the classification advantages of CNNs and Transformer networks for different SNR ratios. The parameters of FCT will be adjusted dynamically to achieve lower loss or better classification accuracy during the training process. The FCT model is verified on a public dataset, showing better performance than current state-of-the-art (SOTA) models of the mixed radio signals, especially at low SNR signals. The best classification accuracy of the FCT can reach 95.70% when the signals are at high SNR. The overall classification accuracy of FCT can reach 84.04%, which is higher than current SOTA models by 26.12%. Theoretical analysis and simulation experiments show that the proposed FCT model provides a new research direction in the classification of mixed radio signals. Full article

This paper presents an autonomous perfectly secure low-bit-rate voice communication system (APS-VCS) based on the mixed-excitation linear prediction voice coder (MELPe), Vernam cipher, and sequential key distillation (SKD) protocol by public discussion. An authenticated public channel can be selected in a wide range, from internet connections to specially leased radio channels. We found the source of common randomness between the locally synthesized speech signal at the transmitter and the reconstructed speech signal at the receiver side. To avoid information leakage about open input speech, the SKD protocol is not executed on the actual transmitted speech signal but on artificially synthesized speech obtained by random selection of the linear spectral pairs (LSP) parameters of the speech production model. Experimental verification of the proposed system was performed on the Vlatacom Personal Crypto Platform for Voice encryption (vPCP-V). Empirical measurements show that with an adequate selection of system parameters for voice transmission of 1.2 kb/s, a secret key rate (KR) of up to 8.8 kb/s can be achieved, with a negligible leakage rate (LR) and bit error rate (BER) of order ${10}^{-3}$ for various communications channels, including GSM 3G and GSM VoLTE networks. At the same time, by ensuring perfect secrecy within symmetric encryption systems, it further highlights the importance of the symmetry principle in the field of information-theoretic security. To our knowledge, this is the first autonomous, perfectly secret system for low-bit-rate voice communication that does not require explicit prior generation and distribution of secret keys. Full article

A filter-free, image-reject, sub-harmonic downconverted RoF link is proposed based on a dual-polarization quadrature phase-shift keying (DP–QPSK) modulator. At the remote antenna unit, the receiving radio frequency signal is applied to the upper QPSK modulator to achieve carrier-suppressed single-sideband (CS–SSB) modulation. The local oscillator (LO) is applied to the lower QPSK modulator, achieving sub-harmonic single-sideband (SH–SSB) modulation. The I/Q mixing is realized by exploiting a two-channel photonic microwave phase shifter, which mainly consists of a modulator, two polarization controllers, and two polarizers. The image interference signal can be rejected when combing the I and Q IF signals through a 90° electrical hybrid. Because the scheme is simple and filter-free, it has a good image-reject capability over a large frequency tunable range. Moreover, due to the special SH-SSB modulation, the modulated signals are immune to the chromatic dispersion-introduced power fading effect. Last, the sub-harmonic downconverter can decrease the frequency requirement of the LO signal. Experimental results show that an image rejection ratio (IRR) greater than 50 dB can be achieved when transmitted through a 25 km single-mode fiber (SMF). Simultaneously, under different RF signals and IF signals, the IRR has no periodic power fading, only small fluctuations. Image rejection capability of the scheme for the 50-MBaud 16-QAM wideband vector signal is also verified and the demodulation of the desired IF signal with a good EVM of less than 5% is realized. Full article

Electromagnetic waves (EMWs) have become an integral part of our daily lives, but they are causing a new form of environmental pollution, manifesting as electromagnetic interference (EMI) and radio frequency signal leakage. As a result, the demand for innovative, eco-friendly materials capable of blocking EMWs has escalated in the past decade, underscoring the significance of our research. In the realm of modern science, the creation of new materials must consider the starting materials, production costs, energy usage, and the potential for air, water, and soil pollution. Herein, we utilized biowaste materials generated during the distillation of fruit schnapps. The biowaste from apple and quince schnapps distillation was used as starting material, mixed with KOH, and carbonized at 850 °C, in a nitrogen atmosphere. The structure of samples was investigated using various techniques (infrared, Raman, energy-dispersive X-ray, X-ray photoelectron spectroscopies, thermogravimetric analysis, BET surface area analyzer). Encouragingly, these materials demonstrated the ability to block EMWs within a frequency range of 8 to 12 GHz. Shielding efficiency was measured using waveguide adapters connected to ports (1 and 2) of the vector network analyzer using radio-frequency coaxial cables. At a frequency of 10 GHz, carbonized biowaste blocks 78.5% of the incident electromagnetic wave. Full article

To address the issue of GNSS receivers being unable to function properly in tunnels due to the loss of Global Navigation Satellite System (GNSS) signals, this paper proposes a two-dimensional differential positioning system for tunnel environments based on dual leaky coaxial (LCX) cables with GNSS signal frequency relay forwarding. The system receives mixed GNSS signals from open environments and utilizes the frequency selection capabilities of the MAX2769E chip to separate and generate radio frequency signals at different frequencies corresponding to GPS, BDS, and GLONASS. These signals are then used to drive three ports of the LCX cables, which are laid in parallel within the tunnel. By leveraging the uniform radiation characteristics of the LCX cables, stable GNSS signal coverage is achieved throughout the tunnel. On the receiving end, the GNSS receiver achieves two-dimensional positioning by utilizing inter-satellite pseudorange differences and reference point error correction. The simulation results indicate that the dual T-shaped radiating LCX cables configuration offers excellent positioning accuracy and noise resistance, achieving meter-level positioning accuracy in tunnel environments. Full article

This work addresses the optimized planning of survivable optical 5G Xhaul access networks employing passive Wavelength Division Multiplexing (WDM) technologies. Specifically, it focuses on the reliability of optical transmission paths connecting remote radio sites to a central hub ensured by using a novel, cost-effective, flexible, and dedicated path protection (DPP-F) scheme, protecting against single-link failures. The proposed DPP-F network protection approach allows for switching of individual wavelengths or the complete multiplexed WDM signal, flexibly applying the best switching option according to given traffic demands. Concurrently, it enables traffic aggregation on the transmission paths from the end and intermediate nodes to minimize the overall network deployment cost. The problem of selecting primary (working) and backup (protection) paths, together with the selection of the best switching and traffic aggregation options, is modeled and solved as a mixed-integer linear programming (MILP) optimization problem. To evaluate the cost savings achieved with DPP-F, we compare it with two reference DPP schemes based on switching the entire multiplexed WDM signal (DPP-M) and individual wavelengths (DPP-W). Numerical experiments conducted across a wide range of network scenarios reveal, among other things, that DPP-F’s performance is at least as good as that of the reference methods, bringing significant cost savings (from several to tens of percent) in most of the analyzed network scenarios. Full article

Radio spectrum resources are very limited and have become increasingly tight in recent years, and the exponential growth of various frequency-using devices has led to an increasingly complex and changeable electromagnetic environment. Wireless channel complexity and uncertainty have increased dramatically, and automated modulation recognition (AMR) performs poorly at low signal-to-noise ratios. It is proposed to use convolutional bidirectional long short-term memory deep neural networks (CNN-BiLSTM-DNNs) as a deep learning framework to extract features from single and mixed in-phase/orthogonal (I/Q) symbols in modulated data. The framework combines the capabilities of one- and two-dimensional convolution, a bidirectional long short-term memory network, and a deep neural network more efficiently, extracting characteristics from the perspective of time and space to enhance the accuracy of automatic modulation recognition. Modulation recognition experiments on the benchmark datasets RML2016.10b and RML2016.10a show that the average recognition accuracies of the proposed model from −20 dB to 18 dB are $64.76\%$ and $62.73\%$, respectively, and the improvement ranges of modulation recognition accuracy are 0.29−5.56% and 0.32−4.23% when the signal-to-noise ratio (SNR) is −10 dB to 4 dB, respectively. The CNN-BiLSTM-DNN model outperforms classical models such as MCLDNN, MCNet, CGDNet, ResNet, and IC-AMCNet in terms of modulation type recognition accuracy. Full article

A 30~60 GHz broadband down-conversion mixer driven by low local oscillator (LO) power is presented. The down-conversion mixer utilizes an input signal coupling technique based on the Marchand balun to achieve broadband operation and achieves low LO power drive and low DC power consumption through the use of a weak inversion bias with Gilbert switching devices. The broadband conversion of single-ended to differential signals is achieved using the Marchand balun with compensation lines, and an equivalent circuit analysis is performed. For the intermediate frequency (IF) output, a self-biased IF trans-impedance amplifier with current reusing and an active IF balun structure are used to achieve signal amplification and single-ended signal output. Test results show that the proposed mixer achieves a conversion gain of −1.2 to 6.4 dB in an IF output bandwidth of 0.1 to 5 GHz at radio frequency (RF) input frequencies of 30 to 60 GHz and LO driving power of −10 dBm. The DC power consumption of the core mixing unit of the proposed mixer is 4.8 mW, and the DC power consumption including the IF amplifier is 28.3 mW. The proposed mixer uses a 65 nm CMOS technology with a chip area of 0.26 mm². Full article

We explore security aspects of a new computing paradigm that combines novel memristors and traditional Complimentary Metal Oxide Semiconductor (CMOS) to construct a highly efficient analog and/or digital fabric that is especially well-suited to Machine Learning (ML) inference processors for Radio Frequency (RF) signals. Analog and/or hybrid hardware designed for such application areas follows different constraints from that of traditional CMOS. This paradigm shift allows for enhanced capabilities but also introduces novel attack surfaces. Memristors have different properties than traditional CMOS which can potentially be exploited by attackers. In addition, the mixed signal approximate computing model has different vulnerabilities than traditional digital implementations. However both the memristor and the ML computation can be leveraged to create security mechanisms and countermeasures ranging from lightweight cryptography, identifiers (e.g., Physically Unclonable Functions (PUFs), fingerprints, and watermarks), entropy sources, hardware obfuscation and leakage/attack detection methods. Three different threat models are proposed: (1) Supply Chain, (2) Physical Attacks, and (3) Remote Attacks. For each threat model, potential vulnerabilities and defenses are identified. This survey reviews a variety of recent work from the hardware and ML security literature and proposes open problems for both attack and defense. The survey emphasizes the growing area of RF signal analysis and identification in terms of commercial space, as well as military applications and threat models. We differ from other recent surveys that target ML, in general, neglecting RF applications. Full article

The multibeam high-throughput satellites (HTS) are regarded as a crucial component in the forthcoming space-based Internet of Things (S-IoT) network. The multi-band frequency conversion capability of HTS is essential for achieving high-capacity information interconnection in the S-IoT network. To enhance the frequency conversion capability of the on-board payload, a reconfigurable local oscillator (LO) harmonic mixer with simultaneous phase shifting and image-rejection is proposed and demonstrated based on a polarization division multiplexing dual-parallel Mach–Zehnder modulator (PDM-DPMZM). By adjusting the input radio frequency (RF) signal and direct current (DC) bias voltage of the modulator, four different LO frequency-multiplication mixing functions can be achieved. The phase of the generated signal can be flexibly tuned over a full 360° range by controlling the angle α between the polarization direction of the polarizer and one axis of the modulator, and it has a flat amplitude response. When combined with an optical frequency comb, the scheme can be extended to a multi-channel multi-band frequency conversion system with an independent phase tuning capability. Additionally, by adjusting the phase difference between dual channel output signals, it can be reconfigured to implement in-phase/quadrature (I/Q) mixing, double-balanced mixing and image-reject mixing. Full article

Cooperative communication and cognitive radio can effectively improve spectrum utilization, coverage range, and system throughput of vehicular networks, whereas they also incur several security issues and wiretapping attacks. Thus, security and threat detection are vitally important for such networks. This paper investigates the secrecy and throughput performance of an underlay cooperative cognitive vehicular network, where a pair of secondary vehicles communicate through a direct link and the assistance of a decode-and-forward (DF) secondary relay in the presence of Poisson-distributed colluding eavesdroppers and under an interference constraint set by the primary receiver. Considering mixed Rayleigh and double-Rayleigh fading channels, we design a realistic relaying transmission scheme and derive the closed-form expressions of secrecy and throughput performance, such as the secrecy outage probability (SOP), the connection outage probability (COP), the secrecy and connection outage probability (SCOP), and the overall secrecy throughput, for traditional and proposed schemes, respectively. An asymptotic analysis is further presented in the high signal-to-noise ratio (SNR) regime. Numerical results illustrate the impacts of network parameters on secrecy and throughput and reveal that the advantages of the proposed scheme are closely related to the channel gain of the relay link compared to the direct link. Full article

This work investigates the influence of altered engine oil on the tribological performance, focusing in particular on wear and interconnected tribofilm formation. For this purpose, Zinc dialkyldithiophosphate (ZDDP) additivated engine oils of different degradation levels, produced in an artificial oil alteration process, were used in tribometer tests with a nitride steel piston ring against a grey cast iron cylinder liner model contact. Parameters were chosen to simulate the boundary and mixed lubrication regime typical for the top dead centre conditions of an internal combustion engine of a passenger car. Wear of the cylinder liner specimens was continuously monitored during the tribometer tests by the radio-isotope concentration (RIC) method, and tribofilms were posteriorly investigated by X-ray photoelectron spectroscopy (XPS). The results clearly show that the steady-state wear rates for experiments with altered lubricants were significantly lower than for the experiments with fresh lubricants. XPS analysis on the formed tribofilms revealed a decrease in sulphide and an increase in sulphate states for altered oils evaluated at 120 °C oil temperature, correlating with a decrease in steady-state wear rate. This finding emphasizes the role of sulphate species in the tribofilm formation process and its anti-wear capabilities, in contrast to the sulphide species and the (poly-)phosphate species, as outlined in most of the ZDDP literature. Moreover, the RIC signal that represents the amount of wear in the engine oil showed a decrease over time for specific altered lubricants and test conditions. These “negative” trends in the wear signal are remarkable and have been identified as an incorporation of wear particles from the lubricant into the tribofilm. This finding is supported by XPS results that detected an iron-oxide layer with a remarkably similar quantity within the tribofilm on the surface. Based on these findings, an assessment of the minimum film formation rate and particle incorporation rate was achieved, which is an important basis for adequate tribofilm formation and wear models. Full article

This paper proposes and studies the physical layer security of a mixed radio frequency/free space optical (RF/FSO) system based on reconfigurable intelligent surface (RIS)-aided jamming to prevent eavesdropping. This work considers Nakagami-m fading for the RF links and Málaga (M) turbulence for the FSO links. A two-hop decode-and-forward (DF) relaying method was used and the eavesdropper actively eavesdropped on the information transmitted by the RF link. The eavesdropper was thwarted by a wireless-powered jammer that transmits jamming signals, which were reflected by the RIS to the eavesdropper to ensure secure communication in the mixed RF/FSO system. The expressions of secrecy outage probability (SOP) and average secrecy capacity (ASC) of the RIS-aided mixed RF/FSO system were derived for the system model discussed above. The Monte Carlo method was utilized to verify the accuracy of these expressions. In the RIS-aided mixed RF/FSO system, the effects of the time switching factor, energy conversion efficiency, and average interference noise ratio on the system secrecy outage probability were analyzed and compared to the RIS-free mixed RF/FSO system. Meanwhile, the influence of the number of RIS reflecting elements, link distances before and after reflection, and fading severity parameter on the security performance of a mixed RF/FSO system assisted by RIS were also investigated. Full article

Single photon ionization (SPI) based on vacuum ultraviolet (VUV) lamps has been extensively investigated and applied due to its clean mass spectra as a soft ionization method. However, the photon energy of 10.6 eV and photon flux of 10¹¹ photons s⁻¹ of a commercial VUV lamp limits its range of ionizable analytes as well as its sensitivity. This work designs a chemical ionization focusing integrated (CIFI) ionization source time-of-flight mass spectrometry (TOFMS) based on a VUV lamp for the detection of volatile organic compounds (VOCs) and oxygenated volatile organic compounds (OVOCs). The photoelectrons obtained from the VUV lamp via the photoelectric effect ionized the oxygen and water in the air to obtain the reagent ions. The ion–molecule-reaction region (IMR) is constituted by a segmented quadrupole that radially focuses the ions using a radio-frequency electric field. This significantly enhances the yield and transport efficiency of the product ions leading to a great improvement in sensitivity. As a result, a 44-fold and 1154-fold increase in the signal response for benzene and pentanal were achieved, respectively. To verify the reliability of the ionization source, the linear correspondence and repeatability of benzene and pentanal were investigated. Satisfactory dynamic linearity was obtained in the mixing ratio range of 5–50 ppbv, and the relative standard deviation (RSD) of inter-day reached 3.91% and 6.26%, respectively. Finally, the CIFI−TOFMS was applied to the determination of OVOCs, and the LOD of 12 types of OVOCs reached the pptv level, indicating that the ionization source has the potential for accurate and sensitive online monitoring of atmospheric OVOCs. Full article

With radio frequency identification (RFID) becoming a popular wireless technology, more and more relevant applications are emerging. Therefore, anti-collision algorithms, which determine the time to tag identification and the accuracy of identification, have become very important in RFID systems. This paper presents the algorithms of ALOHA for randomness, the binary tree algorithm for determinism, and a hybrid anti-collision algorithm that combines these two algorithms. To compensate for the low throughput of traditional algorithms, RFID anti-collision algorithms based on blind source separation (BSS) are described, as the tag signals of RFID systems conform to the basic assumptions of the independent component analysis (ICA) algorithm. In the determined case, the ICA algorithm-based RFID anti-collision method is described. In the under-determined case, a combination of tag grouping with a blind separation algorithm and constrained non-negative matrix factorization (NMF) is used to separate the multi-tag mixing problem. Since the estimation of tag or frame length is the main step to solve the RFID anti-collision problem, this paper introduces an anti-collision algorithm based on machine learning to estimate the number of tags. Full article