Constellation Diagram Research Articles

Evaluation of the Digital Data Performance Transmission on a Fiber-Radio System

This work shows a set of numerical simulations to evaluate the digital data performance transmission for a fiber-radio system. For this goal, VPI Photonics software is used to simulate the transmission of a digital signal at a bit rate of 2.4 GHz through an optical link of 25 km of single-mode standard fiber (SM-SF). Whereas MATLAB software is utilized to emulate a wireless channel considering four main phenomena inherent to this channel, such as multipath and slow fading, co-channel interference, and additive white Gaussian noise (AWGN). The modeling carried out in MATLAB considers a user in movement within its coverage area cell. The constellation diagram, bit error rate (BER), and signal-to-interference-plus-noise ratio (SINR) statistical tests are used to evaluate the performance of this approach. This proposal finds potential applications in the field of cellular telephony, the Internet of Things (IoT), or any other communication system to evaluate the quality of the delivered signal to a user in motion within a particular coverage cell.

Increasing Physical Layer Security Through Hyperchaos in VLC Systems

Visible Light Communication (VLC) systems have relatively higher security compared with traditional Radio Frequency (RF) channels due to line-of-sight (LOS) propagation. However, they still are susceptible to eavesdropping. The proposed solution of the papers have been built on existing work on hyperchaos-based security measure to increase physical layer security from eavesdroppers. A fourth-order Henon map is used to scramble the constellation diagrams of the transmitted signals. The scramblers change the constellation symbol of the system using a key. That key on the receiver side de-scrambles the received data. The presented modulation scheme takes advantage of a higher degree of the map to isolate the data transmission to a single dimension, allowing for better scrambling and synchronization. A sliding mode controller is used at the receiver in a master-slave configuration for projective synchronization of the two Henon maps, which helps de-scramble the received data. The data is only isolated for the users aware of the key for synchronization, providing security against eavesdroppers. The proposed VLC system is compared against various existing approaches based on various metrics. An improved Bit Error Rate and a lower information leakage are achieved for a variety of modulation schemes at an acceptable Signal-to-Noise Ratio.

Demonstrating a real–time QAM–16 visible light communications utilizing off-the-shelf hardware

This study aims to design, implement, and demonstrate a one-way real-time VLC system through DCO-OFDM using QAM–16 as the Mapper (Modulator). The system was divided into two units, namely VLC Server (transmitter) and Client (receiver), based on a low-bit rate transmission applications with running text, which is often found in digital signage. The uniqueness of this report focuses on the use of low-cost components for VLC transceivers, including DSP and ASP. In this condition, DCO-OFDM digital blocks were embedded within the generic-purpose Microcontroller as DSP (STM32F4 Development board), with available and widely assessed libraries being utilized for implementation in the proposed system, to speed up the DSP development process. Meanwhile, the ASP contained a linear LED driver as a substitute for the commercial Bias-T function, as well as the AFE receiver with the Op-Amps circuit blocks. The developmental approach of the VLC system was also carried out by simulation and realistic implementation, where the productions of the DSP and ASP sections, especially the DCO-OFDM with the QAM–16 mapper and the AFE receiver, were performed on MATLAB® and SPICE®, respectively. Additionally, the transfer of QAM–16 modulated data was realistically performed by observing the impact of the optical channel distance changes on the behavior of the constellation diagram.

Identification of advanced optical modulation formatand estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network

In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.

Multimodal attention-based deep learning for automatic modulation classification

Wireless Internet of Things (IoT) is widely accepted in data collection and transmission of power system, with the prerequisite that the base station of wireless IoT be compatible with a variety of digital modulation types to meet data transmission requirements of terminals with different modulation modes. As a key technology in wireless IoT communication, Automatic Modulation Classification (AMC) manages resource shortage and improves spectrum utilization efficiency. And for better accuracy and efficiency in the classification of wireless signal modulation, Deep learning (DL) is frequently exploited. It is found in real cases that the signal-to-noise ratio (SNR) of wireless signals received by base station remains low due to complex electromagnetic interference from power equipment, increasing difficulties for accurate AMC. Therefore, inspired by attention mechanism of multi-layer perceptron (MLP), AMC-MLP is introduced herein as a novel AMC method for low SNR signals. Firstly, the sampled I/Q data is converted to constellation diagram, smoothed pseudo Wigner-Ville distribution (SPWVD), and contour diagram of the spectral correlation function (SCF). Secondly, convolution auto-encoder (Conv-AE) is used to denoise and extract image feature vectors. Finally, MLP is employed to fuse multimodal features to classify signals. AMC-MLP model utilizes the characterization advantages of feature images in different modulation modes and boosts the classification accuracy of low SNR signals. Results of simulations on RadioML 2016.10A public dataset prove as well that AMC-MLP provides significantly better classification accuracy of signals in low SNR range than that of other latest deep-learning AMC methods.

Modulation Format Identification Based on Signal Constellation Diagrams and Support Vector Machine

In coherent optical communication systems, where multiple modulation formats are mixed and variable, the correct identification of signal modulation formats provides the foundation for subsequent performance improvement using digital algorithms. A modulation format identification (MFI) scheme based on signal constellation diagrams and support vector machine (SVM) is proposed. Firstly, the signal constellation diagrams are divided by the fractal dimension of the weighted linear least squares (WLS-FD) algorithm, and the fractal dimension (FD) in each region is calculated, which is regarded as one of the image features. Then, the feature values of the image in different directions are extracted by the gray-level co-occurrence matrix (GLCM), and their mean and variance are calculated, which is regarded as another feature. Finally, the two features are input into the modulation format classifier constructed by the SVM to achieve MFI in coherent optical communication systems. To verify the feasibility and superiority of the scheme, we compare it with the MFI scheme based on higher-order statistical (HOS) features, GLCM features, and FD features, respectively. Further, we built a 30 GBaud coherent optical communication system with fiber lengths of 80 km and 120 km, where the optical signal-to-noise ratio (OSNR) ranges from 0 dB to 30 dB. The proposed MFI scheme identifies seven modulation formats: QPSK, 8QAM, 16QAM, 32QAM, 64QAM, 128QAM, and 256QAM. The results show that compared with the other three schemes, our proposed scheme has a better identification accuracy at low OSNR. In addition, the identification accuracy of this scheme can reach 100% when the OSNR ≥ 10 dB.

Performance Evaluation of NRZ-OOK and Carrier-Less Amplitude Phase Modulation in Li-Fi Environment

Light Fidelity (Li-Fi) is a wireless technology that utilizes light emitting diodes to convey data. Li-Fi has emerged as a promising alternative to radio frequency communication technology in recent years. This paper examines the performance characterisation between NRZ-OOK and carrier-less amplitude-phase modulation (CAP-2, CAP-4 and CAP-8), within a simulated Li-Fi environment. For the parameters of interest, the eye pattern, bit error rate (BER) and constellation diagram are reported. As no distance is specified in the simulation, the BER for CAP is zero, allowing an ideal transmission to be emulated. The scatter diagram increases as the number of CAP modulations increases. For the eye pattern, CAP modulation provides a better visual representation of how noise might affect system performance compared to OOK modulation.

Radio Frequency Fingerprinting Exploiting Non-Linear Memory Effect

Radio frequency fingerprint (RFF) identification distinguishes wireless transmitters by exploiting their hardware imperfection that is inherent in typical radio frequency (RF) front ends. This can reduce the risks for the identities of legitimate devices being copied, or forged, which can also occur in conventional software-based identification systems. This paper analyzes the feasibility of device identification exploiting the unique non-linear memory effect of the transmitter RF chains consisting of matched pulse shaping filters and non-linear power amplifiers (PAs). This unique feature can be extracted from the received distorted constellation diagrams (CDs) with the help of image recognition-based classification algorithms. In order to validate the performance of the proposed RFF approach, experiments are carried out in cabled and over the air (OTA) scenarios. In the cabled experiment, the average classification accuracy among systems of 8 PAs (4 PAs of the same model and the other 4 of different models) is around 92% at signal to noise ratio (SNR) of 10 dB. For the OTA line-of-sight (LOS) scenario, the average classification accuracy is 90% at SNR of 10 dB; for the non-line-of-sight (NLOS) scenario, the average classification accuracy is 79% at SNR of 12 dB.

Power Sparse Code Division Non-Orthogonal Multiple Access Scheme for Next-Generation Flexible Optical Access

In this paper, a power sparse code division non-orthogonal multiple access (PSCD-NOMA) scheme is proposed for next-generation flexible optical access, which is based on the multi-stack network. All the optical network units (ONUs) are allocated with different power levels according to path loss to achieve power multiplexing. Compared with sparse code multiple access (SCMA), the proposed sparse code considers users’ information as a whole, and a larger number of users can be implemented with relatively low complexity and similar or better performance. It can achieve 150% overload by sparse code. Moreover, an irregular-shaped 8 quadrature amplitude modulation (IS-8QAM) constellation diagram is devised that is more suitable for PSCD-NOMA. Orthogonal chirp division multiplexing (OCDM) is employed for its advantages of the stronger anti-dispersion ability. A 10.23 Gb/s PSCD-NOMA transmission over 25 km standard single-mode fiber is experimentally demonstrated. The results show that IS-8QAM can get at least 0.6 dB gain than the conventional 8QAM scheme. The difference in sensitivity between ONUs of different power levels is 2.8 dB, which corresponds to the loss between different paths. The sensitivity difference between different users at the same power level is only 0.3 dB, which indicates the proposed PSCD-NOMA has good fairness. PSCD-NOMA is a potential method for future optical access systems due to its great performance.

Joint modulation format recognition and optical performance monitoring for efficient fiber-optic communication links using ensemble deep transfer learning

We propose an ensemble deep transfer learning (EDTL) method, which is a more refined multilayer feature extraction achieved by aggregating the convolutional layers of pretrained convolutional neural network models for joint optical modulation format recognition (MFR) and optical performance monitoring (OPM) in fiber-optic communication links. Modulation formats, such as quadrature phase shift keying, 16 quadrature amplitude modulation (QAM), and 64 QAM, are monitored for the optical signal-to-noise ratio (OSNR) range of 20 to 30 dB by considering the dispersive effects of chromatic dispersion from 0 to 1200 ps / nm and polarization mode dispersion from 10 to 70 ps in the fiber-optic transmission path. First, the generated constellation diagrams affected by the impairments are used to optimize and evaluate the pretrained models based on classification targets. Then the proposed EDTL model is designed by aggregating the feature extractor parts of the pretrained models; it is implemented in three phases, and the results are comprehensively studied. Further, data augmentation and aggregation methods are introduced to enhance the performance of joint MFR and OPM. The results obtained prove that the proposed model provides faster convergence of MFR and better identification accuracy of OSNR toward optical signal diagnostics in optical networks for efficient optical link monitoring.

QPSK to BPSK modulation format conversion byphase-sensitive parametric amplification inmulti-slot waveguides.

The optical modulation conversion of a quadrature phase-shift keying (QPSK) signal to two binary phase-shift keying (BPSK) signals is theoretically realized in a phase-sensitive amplification (PSA) conversion system. We propose a multi-slot silicon-carbon nanotube/polydimethylsiloxane hybrid waveguide to achieve high nonlinearity of 108 W -1 m -1 for improving PSA performance. Constellation diagrams, error vector magnitude, and bit error rate (BER) are used to investigate and measure the performance of the output. The results show that the converted BPSK signal has more than a 10dB higher signal-to-noise ratio than the QPSK signal with a BER threshold of 10-3. Furthermore, the system has great potential for hierarchical modulation of advanced format signals and long-distance transmission.

STO Estimation for OFDM System Using CDM

In this letter, we propose a novel scheme for symbol timing offset (STO) estimation by using a convolutional neural network (CNN)-deep neural network (DNN) model (CDM) architecture for the orthogonal frequency division multiplexing (OFDM) system over different fading channel models. The proposed scheme estimates STO in the presence of carrier frequency offset (CFO) and without prior knowledge of the channel, modulation format, and transmitted OFDM signal parameters. The CDM architecture achieves better estimation accuracy gain compared to that of statistical-based and pilot-assisted CNN-based methods. Finally, the proposed CDM is validated over a radio frequency testbed, and the desired constellation diagram is obtained.

Self-healing integration of fiber/FSO communication and sensor network for improving survivability

In this paper, we proposed a novel self-healing fiber/free-space optics (FSO) communication integrated with a sensor network for simultaneous transmission of optical communication signal and sensing signal as well as a function of fault protection by using a coarse wavelength-division multiplexer (CWDM). The combination of CWDM and fiber integrated with FSO transmission channel is incorporated to design a cost-effective, flexible network and increase the coverage of transmission distance. The experiment result proves that the fiber integrated with an FSO transmission channel achieves a clear constellation diagram and best error vector magnitude (EVM) value. On the other hand, the scanning-laser interrogation system is used for data acquisition in the strain-based fiber Bragg grating (FBG) sensor system. Moreover, the radial basis function neural network (RBFNN) algorithm is used to estimate the central wavelength of FBGs.

Mitigating Crosstalk of Vortex Beam Within an OAM-Mode Group Over an OAM Fiber by Reversing Transmission Matrix

For enhancing channel capacity and spectrum efficiency, vortex beam carrying orbital angular momentum (OAM) has been ardently investigated in optical communication. However, the crosstalk among OAM modes induced by mode coupling has seriously hindered the development of vortex beam communication (OBC). A novel concept of mitigating crosstalk within an OAM-mode group for a short-haul optical fiber communication (OFC) is proposed by reversing the transmission matrix. To support more OAM modes propagation, a step-index OAM fiber with ring-shaped profile is also designed and fabricated, which can support 6 vector/OAM modes propagation. A proof-of-concept experiment is performed for verifying the feasibility of the proposed concept, where a quadrature-phase-shift-keying (QPSK) signal is utilized as an excitation signal. Bit-error-ratio (BER) and constellation diagram (CD) are employed to evaluate the performance of the proposed system. The captured intensity profiles and interference diagrams demonstrate that the propagated four OAM beams within the 2rd OAM-mode group are successfully received. The measured BER and CD illustrate that the crosstalk within the mode group can be significantly mitigated. Further, although the data speed or fiber length is increased, the system performance using the proposed concept is still superior to the case without increasing fiber length or data speed when the proposed concept is missed. Therefore, the proposed concept is effective for mitigating the crosstalk and can be then used for a short-haul OAM-based OFC.

Modulation Classification of Active Attacks in Internet of Things: Lightweight MCBLDN With Spatial Transformer Network

The Internet of Things (IoT) permeates every aspect of our daily lives as billions of interconnected devices are deployed in the physical world. However, IoT networks operate in an untrusted environment and often suffer from many malicious active attacks. Automatic modulation classification (AMC), which can identify the modulation format of intercepted signals without prior knowledge, is a vital technology in countering physical-layer threats of IoT. However, most of the existing algorithms assume the channel is time invariant, and the AMC in time-varying channels is not been well studied. To deal with this dilemma, a novel AMC algorithm MCBLDN consisting of multiple convolutional neural networks (CNNs), a bidirectional long short-term memory network (BLSTM), and a deep neural network (DNN) is proposed. In MCBLDN, a multislot constellation diagram (CD) method is proposed to extract time-evolution characteristics for generating more discriminative features. Specifically, different grayscale subimages generated by slotted CDs are processed serially by their respective CNNs. Therefore, MCBLDN is overparameterized and time consuming. In addition, the frequency offset and phase offset caused by time-varying channels are neglected in MCBLDN, which is detrimental to the performance of AMC. To address the mentioned disadvantages, a lightweight MCBLDN with a spatial transformer network (SLCBDN) is proposed. First, the multiple CNNs in MCBLDN are pruned into a lightweight classification model, and the input data are rearranged to facilitate parallel processing by the lightweight CNN. Additionally, the spatial transformer network (STN) is utilized to reduce the influence of frequency offset and phase offset. Numerical results verify that the proposed method achieves superior performance and higher speed compared to the baseline algorithm MCBLDN.

Metasurface-Based Filters for High Data Rate THz Wireless Communication: Experimental Validation of a 14 Gbps OOK and 104 Gbps QAM-16 Wireless Link in the 300 GHz Band

We demonstrate the effectiveness of frequency selective surface filters in wireless communications at low terahertz (THz) frequencies. Full-wave simulations of pass-band filters designed at 270 GHz and 330 GHz are compared with measurements over 220–360 GHz, showing remarkable agreement. The filter spectral response is used to analytically model a THz filter-based wireless channel for modulated signals. In particular, numerical results and measurements for an OOK modulated signal are in good agreement for both free-space and filtered transmission at 14 Gb/s. In both cases, bit error rates (BER) as low as 10-10 are measured. This result demonstrates that the filters marginally affect the BER with respect to free-space, interference-free transmission, whereas interfering signals are strongly rejected. This result is demonstrated through a systematic evaluation of the BER in presence of an interfering signal with different carriers and amplitudes. Results confirm a strong filter rejection to interference carriers close to the filter central frequency. Conversely, without the filters the BER performance is fully compromised. Finally, we demonstrate numerically and experimentally that the constellation diagram for 104 Gb/s QAM-16 communication is not significantly affected by the filter. The investigated filters may provide a robust approach towards efficient spectrum management for future 6G wireless applications.

Optimal Low Complexity Detector for Signed-Quadrature Spatial Modulation MIMO System

Very recently, signed quadrature spatial modulation (sQSM) is developed as a competent technique that expands the spatial constellation diagram of QSM system by adding a bipolar dimension. Despite the enhanced spectral efficiency, the existing optimal maximum likelihood (ML) presents a serious computational challenge for large scale sQSM systems. Therefore, developing a reduced complexity detector for sQSM schemes is of significant importance to enable implementing and enjoying the inherent advantages of this promising system. Toward this end, a Tree Search (TS) optimal low complexity detector, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSopt</i> , for sQSM Multiple Input Multiple Output (MIMO) system is proposed and analyzed in this paper. The proposed detector expands the computationally complex ML detector for sQSM into a tree-structure representation. The idea of the suggested algorithm is to employ an efficient searching strategy that can expeditiously find the branch corresponding to the minimum error without tracing the entire nodes as in the ML case. It is reported that the proposed TSopt algorithm achieves the exact error performance as ML detector but with substantial reduction in computational complexity. Besides, complexity analysis in terms of the number of visited nodes of the TSopt algorithm is analyzed and a closed-form expression for the expected complexity at high SNR values is derived. Reported results disclose agreement between simulation and expected analytical complexity with substantial gains of around 60–80% in complexity reduction for different system parameters.

Time-variant entropy regulated multiple access for flexible coherent PON.

To further increase the data rate and rate flexibility of next-generation passive optical networks (PONs), we propose a scheme jointing probabilistic constellation shaping (PCS), constellation diagram identification, and coherent detection. The rate of the coherent PON is dynamically regulated with time-variant entropy, and monitored by the image classifier in real-time. The transmitted cascade frames allocated to different optical network units (ONUs) from the optical line terminal (OLT) can be synchronized, identified, and distinguished by the labeled entropy values. The proposed graphic monitoring scheme is experimentally demonstrated based on a dual-polarized coherent optical transmission system. A 0.5 interval value of entropy is precisely tuned to achieve a 96.13% constellation identification accuracy for entropy/rate by a fast normalized cross correlation coefficient (NCC)-based image classifier. A real-time data rate from 350 to 550 Gb/s with PCS-64-quadrature amplitude modulation (64-QAM) format for a single ONU is achieved which can maximally support five independent ONUs with single wavelength and two polarization states and the entropy varying from 3.5 to 5.5 with 0.5 intervals, respectively.

Remote Vital Sign Monitoring With Reduced Random Body Swaying Motion Using Heartbeat Template and Wavelet Transform Based on Constellation Diagrams

Remote vital signs monitoring using millimeter-wave (mmWave) sensors has gained lots of attention because of their contactless, portable advantages. However, their received signals are more sensitive to random body motions (RBM) which degrades the accuracy of heart rate (HR) detection. To overcome this challenge, multi-input multi-output (MIMO) configuration can be used to reduce RBM's impact as each channel has different points of view with respect to the subject under test (SUT). Here we propose the use of a Frequency Modulated Continuous Wave (FMCW) radar from Texas Instruments (TI) at 77 GHz to collect data from its 192-channel configuration. Since vital sign information extracted using Arctangent Demodulation (AD) could be corrupted by either RBM or respiratory harmonics, a method is needed to minimize such effects. Hence, we develop an algorithm where a Heartbeat Template (HBT) is extruded based on the Constellation Diagram that shows the Quadrature signals from the target's range profile. The HBT is then used to design an adapted-wavelet for Continuous Wavelet Transform (CWT) to magnify the heartbeat signals. Under circumstances where RBM overwhelms the heartbeat signal that the HBT cannot completely reduce its effects, a spectral-based HR selection method is also developed to estimate the HR. By employing the proposed methods, we have reduced mean-error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\mu _e}$</tex-math></inline-formula> of HR estimation significantly from 9 bpm to less than 2 bpm.

A Novel Blind High-Order Modulation Classifier Using Accumulated Constellation Temporal Convolution for OSTBC-OFDM Systems

Deep learning (DL)-based blind modulation classification (BMC) has flourished in the single carrier system for low-order modulation signals. However, the research of DL-based BMC has not been well explored in the OSTBC-OFDM system (Orthogonal Space-Time Block Coded-Orthogonal Frequency Division Multiplexing), especially high-order modulation types. This brief proposes a novel BMC algorithm for high-order modulation signals (e.g., 4096QAM), which is based on reconstruction processing and accumulation constellation temporal convolution BMC network (ACTC-BMCNet). First, we employ the blind zero-forcing (ZF) equalization algorithm to reconstruct the damaged signal and enhance the signal representation power. Then, the constellation accumulation strategy is executed on the raw constellation diagram (CD) to generate the accumulated CD (ACD) image for reducing constellation dispersion. After that, instead of traditional 2D convolution, the constellation temporal convolution enabled in ACTC-BMCNet is leveraged to reduce computational overhead and extract the more discriminative feature. Monte-Carlo experiments are performed on fourteen modulation signals, and results indicate that the proposed ACTC-BMCNet possesses a superior classification accuracy and a faster recognition speed than the DL-based benchmark classifiers.