Modulation Scheme Research Articles

Enhancing Security of Telemedicine Data: A Multi-Scroll Chaotic System for ECG Signal Encryption and RF Transmission.

Protecting sensitive patient data, such as electrocardiogram (ECG) signals, during RF wireless transmission is essential due to the increasing demand for secure telemedicine communications. This paper presents an innovative chaotic-based encryption system designed to enhance the security and integrity of telemedicine data transmission. The proposed system utilizes a multi-scroll chaotic system for ECG signal encryption based on master-slave synchronization. The ECG signal is encrypted by a master system and securely transmitted to a remote location, where it is decrypted by a slave system using an extended state observer. Synchronization between the master and slave is achieved through the Lyapunov criteria, which ensures system stability. The system also supports Orthogonal Frequency Division Multiplexing (OFDM) and adaptive n-quadrature amplitude modulation (n-QAM) schemes to optimize signal discretization. Experimental validations with a custom transceiver scheme confirmed the system's effectiveness in preventing channel overlap during 2.5 GHz transmissions. Additionally, a commercial RF Power Amplifier (RF-PA) for LTE applications and a development board were integrated to monitor transmission quality. The proposed encryption system ensures robust and efficient RF transmission of ECG data, addressing critical challenges in the wireless communication of sensitive medical information. This approach demonstrates the potential for broader applications in modern telemedicine environments, providing a reliable and efficient solution for the secure transmission of healthcare data.

THE PERFORMANCE ANALYSIS OF PRECODED SPACE-TIME FREQUENCY MIMO-GFDM OVER RAYLEIGH FADING CHANNELS

The physical layer is implemented in the present communication era with new multicarrier modulation schemes such as Generalized Frequency Division Multiplexing with Multi-Input and Multi-Output (MIMO-GFDM) antenna systems to achieve good spectral efficiency and diversity order. This paper presents precoded Space-Time-Frequency MIMO-GFDM performance analysis to improve the bit error rate performance without increasing transmission power and bandwidth compared to conventional techniques. The proposed system also enhances the diversity order over frequency selective fading channels. In general, we need to perform channel matrix inversion operations at the receiver or channel precoding matrix operations at the transmitter to detect the symbols of MIMO-GFDM systems. This paper's proposed scheme completes the same task without performing channel matrix inversion. Orthogonal transform techniques such as Haar, Harley, Walsh-Hadamard, and Slant transforms are used as precoders at the transmitter for the proposed scheme. The simulation results are validated on the MATLAB working platform. We have compared the bit error rate of the PSTF-MIMO-GFDM system with Space-Time (ST) and Space Frequency (SF) as baseline schemes and different orthogonal transform precoding techniques.

LiPro: displaying concurrent image viewing and imperceptible positioning using visible light

Visible light can be used to achieve effective human interaction with projectors. However, current visible light-based projectors for the target positioning either lose the image viewing function or cause abnormal luminance changes perceived by human eyes, which is not practical for real-world applications. Therefore, we propose a smart lighting-based projector system, LiPro, which can display imperceptible target positioning and any image viewing, simultaneously. In a nutshell, we propose what we believe to be a novel color space conversion modulation scheme to transmit the landmark information, which adds δ and −3δ color intensity value on the red (R) channel and blue (B) channel according to the YCbCr color space analysis, so that human eyes are imperceptible to light intensity changes. Then, we propose the dual-channel decision demodulation scheme, which demodulates the received light signals on both the R channel and B channel at the same time to decide and increase the correctness of the landmark recognition. In the end, we use off-the-shelf devices to implement LiPro to evaluate its performance, and results from extensive experiments show that our LiPro can achieve centimeter-level target positioning under any image viewing conditions.

Quasi-passive modulation for monolithic GaN photoelectronic circuit

Due to the overlap between the electroluminescence spectrum and spectral responsivity curve, gallium nitride (GaN)-based multi-quantum well (MQW) diodes can modulate and detect light emitted by another diode with the same MQW structure. This enables the realization of a monolithic integrated GaN optoelectronic circuit, integrating an MQW-based transmitter, waveguide, modulator, and receiver on a tiny GaN chip. It is well known that the active region of MQW absorbs high-energy photons within the plane, generating electron-hole pairs and forming photogenerated carriers. The change in free carrier concentration causes variations in the refractive index and absorption characteristics of the waveguide, thereby manipulating light propagation within the waveguide. Based on this physical phenomenon, a quasi-passive modulation scheme is proposed for a monolithic GaN photoelectronic circuit. This scheme achieves optical modulation by connecting a variable resistor between the modulator electrodes, avoiding the need for high-precision external electric fields and complex control circuits. The performance of the quasi-passive modulation was tested through on-chip data transmission and real-time audio signal transmission. The results indicate that quasi-passive modulation is highly feasible in optoelectronic systems and shows great promise as a competitive core module for future large-scale photonic integrated circuits.

Spectrum Sensing Method Based on STFT-RADN in Cognitive Radio Networks.

To address the common issues in traditional convolutional neural network (CNN)-based spectrum sensing algorithms in cognitive radio networks (CRNs), including inadequate signal feature representation, inefficient utilization of feature map information, and limited feature extraction capabilities due to shallow network structures, this paper proposes a spectrum sensing algorithm based on a short-time Fourier transform (STFT) and residual attention dense network (RADN). Specifically, the RADN model improves the basic residual block and introduces the convolutional block attention module (CBAM), combining residual connections and dense connections to form a powerful deep feature extraction structure known as residual in dense (RID). This significantly enhances the network's feature extraction capabilities. By performing STFT on the received signals and normalizing them, the signals are converted into time-frequency spectrograms as network inputs, better capturing signal features. The RADN is trained to extract abstract features from the time-frequency images, and the trained RADN serves as the final classifier for spectrum sensing. Experimental results demonstrate that the STFT-RADN spectrum sensing method significantly improves performance under low signal-to-noise ratio (SNR) conditions compared to traditional deep-learning-based methods. This method not only adapts to various modulation schemes but also exhibits high detection probability and strong robustness.

Transmission Diversity by Alamouti-Coding, Propagation Control by IRS in CDMA-FBMC-OQAM System

This manuscript suggests a modulation scheme for exploiting transmission diversity by integrating Alamouti coding and controlling the transmission by intelligent reflecting surface (IRS) in code division multiple access filter bank multicarrier offset quadrature amplitude modulation (CDMA-FBMC-OQAM) system, a promising technique for future-generation wireless communication. To neutralize the impact of channel phases, the IRS elements are in charge of intelligently adjusting the incident signal phases. By maximizing the signal-to-noise-ratio (SNR) of the received signal, the bit-error-rate (BER) performance is improved. By changing the number of IRS elements, taking into account various channel scenarios and modulation schemes, we compare the BER performance of the proposed system with that of the conventional Alamouti coded CDMA-FBMC-OQAM system in simulation results. The findings demonstrate that the IRS-assisted Alamouti coded CDMA-FBMC-OQAM transmission is a feasible choice for future-generation wireless communication systems.

Intelligent Method for Mutation of Input Cases with Feedback

Relevance. Fuzzing is one of the effective ways to improve the software reliability and is included in the mandatory list of research carried out at the stage of qualification testing according to national standard GOST R 56939-2016. The use of standard mutators reduces the fuzzing process to brute force, which negatively affects the time of incorrect program behavior detection. In this regard, it is important to rationalize the selection of input data, which takes into account the data corpus specifics, as well as the context describing the software response under test and allowing to determine the mutations at the next iteration of testing. Purpose of the research is to increase the efficiency of fuzzing by intellectualizing the standard mutator using neural networks, which takes into account the syntactic and semantic features of the input corpus and uses program feedback.Methods. The methods of analysis and synthesis, theory of algorithms, discrete and computational mathematics, machine learning were used.Result. The advantages and disadvantages of the standard module for AFL fuzzer’s input corpus mutation are considered. The justification of neural network choice with LSTM-architecture as a mechanism that realizes the intelligent control of input corpora’s’ generation and transformation is given. The proposed mutation method is described, which implies the integration of decision making mechanism on the amount and format of necessary mutations to increase the code coverage into the standard mutator, as well as the subsequent refinement of input data by shell-code to check the operability of the fragment that caused abnormal software’s behavior. The scheme of the mutation module is presented, which includes a component of input corporas conversion for generation of program execution traces and a component aimed at concept confirmation and re-call of abnormal software behavior using the generated shell-code.Novelty. Unlike the known ones, the proposed method uses feedback, fixing the software reaction, when forming the data mutation strategy, which determines the scientific novelty of the obtained results.Significance. The proposed solution allows reducing the program testing time while maintaining the code coverage. The results obtained in the research are universal and, in the future, can be used in white, black and gray box fuzzing methods.

Numerical investigation of non-uniform arrays of directive antennas for radiation pattern analysis using spatial modulation schemes

This research presents a novel Non-Uniform Array (NUA) design for Spatial Modulation (SM) systems, aiming to improve Bit Error Rate (BER) performance. The NUA utilizes directional antennas with low-similarity radiation patterns, enabling the receiver to effectively distinguish between signals transmitted from different antenna elements. This enhanced signal discrimination capability directly contributes to improved system capacity and potential spectral efficiency gains for massive MIMO systems employing SM techniques. The proposed NUA was optimized for operation at 5.2 GHz, achieving a high degree of pattern distinguishability. Measured results confirm the effectiveness of the design, demonstrating good agreement with simulations. Moreover, an analysis based on the similarity of the radiation pattern was conducted

Performance evaluation of symbol detection algorithms in massive MIMO communication systems

Abstract Massive MIMO (mMIMO) is the essential technique for attaining the exponential increase in the data rate needed for future communication systems. These advancements have been significantly supported by progress in VLSI technology, which enables the integration of an enormous number of antennas and the complex signal processing essential for massive MIMO systems on a single chip. This work conducts a comprehensive analysis of the intricacy of seven matrix decomposition techniques for symbol detection in future mMIMO communication systems: ADMM-based infinity norm (ADMIN), Neumann series (NS), Newton iteration (NI), Jacobi iteration (Ja), improved Gauss-Seidel (IGS), conjugate gradient (CG), and QR decomposition (QR), against linear and near-optimal minimum mean square error (MMSE). QR, GS, Ja, and CG belong to linear algebraic methods based on matrix decomposition. MMSE and ADMIN are nonlinear optimization methods; NS and NI are iterative methods. The analysis examines into the complexity of these detection algorithms, considering the symbol error rate, the convergence rate, the initial solution vector, and the correlation factor. Performance evaluations are conducted on 8 and 16-user mMIMO systems with 64 and 128 base station antennas, modulation schemes (32-QAM and 64-QAM), iteration counts (p = 1, 2, 3, 4), correlation factors (α = 0.2, 0.4, and 0.6), and initial solution vectors (zero vector, D −1 and W 2 −1 yMF ). The result clearly shows that if the initial solution and number of iterations are chosen properly, the IGS-based linear detector achieves near-optimal performance with a lower computational complexity of O(K 2) compared to the nonlinear MMSE-based detector with a computational complexity of O(K 3).

Novel filtering and regeneration technique with statistical feature extraction and machine learning for automatic modulation classification

Automatic modulation classification (AMC) plays a crucial role in the stages of processing signals from unknown sources and monitoring the airwaves. This paper presents an AMC method based on machine learning (ML) using constellation diagrams, distribution test function and high-order cumulants (HOCs) and novel filtering and regeneration technique. A data clustering approach based on the signal phase characteristics is utilized to perform filtering, regeneration, and testing distribution functions. NI PXIe-1065 was used as a source of modulated signals, which is a multifunctional experimental complex for generating signals with various types of modulation. In this work, 5 types of modulation schemes were employed, specifically BPSK, QPSK, PSK-8, PSK-16 and PSK-32. The proposed classification method consists of three main stages. At the first stage, the constellation diagram is partitioned into clusters. At the second stage, the phase distribution function of signals within each cluster is analyzed, HOCs were calculated and novel filtering and regeneration techniques were employed. The main concept of the proposed approach is compressing angular ranges depicted on the constellation diagram. This involves filtering points and subsequently regenerating, which means relocating points that fall within adjacent phase ranges. Also, a statistical analysis of the phase transition distribution function in clusters is conducted at this step. At the last stage the obtained data set was trained using XGBOOST ML method. The proposed approach shows excellent results and can be a strong contender to existing NN based AMC methods, achieving 95 % accuracy at an SNR value of 9 dB.

Polarized APSK modulation system with polymorphic SC signals

AbstractThis paper discusses a new symbol modulation scheme known as polarized amplitude and phase shift keying, (‐APSK) modulation with four rings in its basic form. The new scheme maps power‐polarized symbol pairs on its constellation in order to increase the number of data symbols. ‐APSK exploits a symbol mapper based on a conjugate power splitting algorithm. Product modulation is applied, where a voltage signal of a specified amplitude and phase is multiplied by another current signal of a specified amplitude and phase, thus forming polymorphic signals in the product constellation. At the receiver, an isolated detection is performed, where the voltage signal is detected independently of the received current signal. A selection combining scheme is then used. The results depict unity peak‐to‐average power ratio and low average symbol energy, which is desirable for Green communications in 5G networks. It presents lower bit error rates when compared with the state‐of‐the art ‐ary quadrature amplitude modulation, with a signal‐to‐noise ratio gap of at least . The proposed analytical framework closely matches the simulations for bit error rates under .

Reflux Power Optimization of a Dual-Active Hybrid Full-Bridge Converter Based on Active Disturbance Rejection Control

The dual-active hybrid full-bridge (H-FDAB) DC–DC converter has great potential in medium-voltage high-power photovoltaic power station applications by introducing a three-level bridge arm to increase the output voltage range. However, its mathematical model and optimum modulation schemes have not been fully explored. Under the traditional PI control, the H-FDAB DC–DC converter will produce significant reflux power, which will lead to a decrease in converter efficiency and output voltage fluctuation. On this basis, this paper proposes a reflux power optimization strategy for an H-FDAB DC-DC converter based on active disturbance rejection control (ADRC). Firstly, the structure and power characteristics of the H-FDAB DC–DC converter are analyzed, and the relationship among the reflux power, the transmission power, and the phase shift angle is derived. Secondly, to reduce the complexity of the control calculation, upon the foundation of dual phase-shifting modulation, the Karush–Kuhn–Tucker (KKT) condition is used to solve for the phase shift angle that corresponds to the minimum reflux power. Simultaneously, we develop an ADRC loop utilizing an extended state observer (ESO) for the real-time estimation of system states. We also consider the sudden changes in input voltage, load switching, and transmission power fluctuations caused by reflux power optimization strategies as system disturbances and compensate for them accordingly. Finally, the experiments conclusively validate the designed control strategy’s correctness and feasibility.

Performance analysis of nonlinear crosstalk of WDM systems using modulation schemes criteria

Abstract Nonlinearities in optical fibers are regarded as the most significant barriers that endanger the effectiveness of the optical transmission system and pose a threat to communication quality. Four-wave mixing (FWM) is one of the most important nonlinear effects that greatly reduces the wavelength-division multiplexing (WDM) system performance at high data rates over extended transmission distances. This research examines, and assesses, numerically, the behavior of a 4-channel, 40 Gbps WDM system under the effect of the FWM under various tuning parameters, including dispersion, input power, and wavelength spacing. The system model was built using OptiSystem software, and then three different modulation formats, namely, Non-return-to-zero-frequency shift keying, Return-to-zero frequency shift keying, and differential phase shift keying (DPSK) are used to assess the FWM power penalty. The results demonstrate that the FWM power penalty obtained with 1 nm wavelength separation in the DPSK method is dramatically reduced to −35 dBm. This study also demonstrates that when power variation is taken into consideration, the DPSK modulation scheme delivers a lower bit error rate in comparison to other modulation schemes.

Reinforcement learning-based automated modulation switching algorithm for an enhanced underwater acoustic communication

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

Individually tunable tunnelling coefficients in optical lattices using local periodic driving

Abstract Ultracold atoms in optical lattices have emerged as powerful quantum simulators of translationally invariant systems with many applications in e.g. strongly-correlated and topological systems. However, the ability to locally tune all Hamiltonian parameters remains an outstanding goal that would enable the simulation of a wider range of quantum phenomena. Motivated by recent advances in quantum gas microscopes and optical tweezers, we here show theoretically how local control over individual tunnelling links in an optical lattice can be achieved by incorporating local time-periodic potentials. We propose to periodically modulate the on-site energy of individual lattice sites and employ Floquet theory to demonstrate how this provides full individual control over the tunnelling amplitudes in one dimension. We provide various example configurations realising interesting topological models such as extended Su–Schrieffer–Heeger models that would be challenging to realise by other means. Extending to two dimensions, we demonstrate that local periodic driving in a Lieb lattice engineers a two-dimensional (2D) network with fully controllable tunnelling magnitudes. In a three-site plaquette, we show full simultaneous control over the relative tunnelling amplitudes and the gauge-invariant flux piercing the plaquette, providing a clear stepping stone to building a fully programmable 2D tight-binding model. We also explicitly demonstrate how utilise our technique to generate a magnetic field gradient in 2D. This local modulation scheme is applicable to many different lattice geometries.

A new modulation for three‐phase current source converter

AbstractThis brief proposes an improved pulse width modulation (PWM) scheme, which uses diode voltage clamping characteristics to achieve the direct control of three‐phase current source converter without any logical transformation. Compared with the traditional indirect carrier modulation scheme—mapping‐PWM, the proposed scheme can solve the disadvantages of low DC current utilization, high switching loss, and complex control process. The generation mechanism of IPWM is analyzed in this brief. Then, based on the generation mechanism, the output characteristics of the system under IPWM are analyzed and compared with the traditional modulation scheme. Finally, the experimental results verify the effectiveness the proposed modulation scheme.

PSF-engineered snapshot full-Stokes polarizing-spectral-imaging via metasurface with reciprocally encoded anisotropic detour phase

A broadband snapshot full-Stokes polarizing-spectral-imaging (PSI) is achieved by reciprocally designing a metal–insulator-metal metasurface with anisotropic detour phase to generate polarizing-spectrally dependent dot-array-shaped point-spread-function (PSF), and the high dimensional information can be retrieved from one snapshot intensity image through the post-computation. It overcomes the restriction on the wavelength selection in principle faced by the propagating phase modulation scheme used in the previous metasurface-based PSI approaches, and brings advantages including compact configuration, fast acquisition and broadband operation. The dependence of the engineered PSF on the polarizing-spectral channel across 100 nm in the visible band is experimentally verified and analyzed, consistent well with the simulation. Reconstruction results for the high dimensional imaging are presented in simulations and experiments based on the established model.

Modulation and Control Schemes of Parallel FCC-CSC with DC Current Balance

Modulation and Control Schemes of Parallel FCC-CSC with DC Current Balance

A robust OFDM IM-QAM NOMA scheme for URLLC and eMBB downlink service coexistence

This work introduces a non-orthogonal multiple access (NOMA) scheme suitable for low-rate ultra-reliable low-latency communication (URLLC) and enhanced mobile broadband (eMBB) services multiplexing in downlink communication. Tailored to minimize mutual interference, URLLC information is conveyed via a modified index modulation (IM) scheme on top of quadrature amplitude modulation (QAM) transferring eMBB traffic. Aiming at providing a proof of concept of the proposed scheme, we calculate the bit error rate of both services over Rayleigh fading with diversity, as well as the eMBB service achievable rate with typical multi-input multi-output (MIMO) configuration when the URLLC service utilizes space–time coding or diversity. The proposed scheme achieves a low bit error rate for the URLLC IM signal, at the cost of a lower information rate, while affecting the performance of the eMBB user mainly due to power sharing among the IM and QAM signals. To further investigate the feasibility of the proposed scheme, we calculate the transmission energy required by the base station to support both services over a typical cellular channel model, for various service requirements and user distances, in comparison to an orthogonal multiple access (OMA) puncturing scheme utilizing time-multiplexing of QAM for the eMBB traffic and BPSK for the URLLC traffic. Overall, our results show that the proposed scheme attains better performance compared to the puncturing scheme and offers a robust solution with easy user pairing for low-rate URLLC and typical eMBB downlink service multiplexing for 5G communications and beyond.

Local fusion generative adversarial network with dual-discriminator and parallel multipath and its application in machinery fault diagnosis with imbalanced data

Abstract Diagnosing faults in critical machinery components is imperative for effective condition monitoring and real-world datasets often suffer from data imbalance. To address this issue, numerous data generation methods have been developed, such as improved local fusion generative adversarial network (ILoFGAN), variational autoencoding GAN (VAEGAN), etc. However, the existing data generation methods primarily concentrate on global and single-scale features and often ignore local or multi-scale features, which leads to the omission of key features or nuances in the generated data. Therefore, a novel approach called the Local Fusion Generative Adversarial Network with Dual-discriminator and Parallel multipath (LoFGAN-DP) is designed to enhance the fault diagnosis performance in the context of imbalanced data. The LoFGAN-DP features a Parallel Multi-Path (PMP) module along with a dual-discriminator scheme, in which the multipath module facilitates feature extraction at various scales through convolution across paths of diverse sizes, and the dual-discriminator scheme can better improve the quality and diversity of the samples generated by the generator. The PMP module and dual-discriminator scheme enhance the proposed method's robustness against variations in input data. After generating data by LoFGAN-DP, a two-dimensional capsule network is further used to achieve the efficient recognition of fault features. To validate the proposed LoFGAN-DP in the machinery fault diagnosis with imbalanced data, the gear dataset and the self-constructed bearing dataset were utilized. Experimental results show that LoFGAN-DP significantly improves Structural Similarity Index (SSIM), Fréchet Inception Distance (FID), and fault classification accuracy compared to several advanced methods.