Coherent Detection Research Articles

Performance evaluation of a 2.56 Tbps DP-16QAM WDM system under various channel spacings and weather conditions using MIMO configurations

Abstract This paper proposes a 16 × 160 Gbps DP-16QAM-WDM-FSO system with 100 and 25 GHz channel spacing, utilizing coherent detection with digital signal processing to enhance the receiver sensitivity. The system’s performance is evaluated under different climate circumstances, such as dust, rain, and fog. The MIMO technique with 2 × 2, 4 × 4, and 8 × 8 configurations are suggested to mitigate weather attenuation effects. Performance metrics, including bit error rate (BER), error vector magnitude (EVM %), quality factor (Q-factor), and optical signal-to-noise ratio (OSNR), are utilized to evaluate the system’s performance. Results show that the proposed system increases the maximum transmission distance by 12.67 %–49.33 %, 19.17 %–78.66 %, and 25.68 %–108 % for all weather circumstances when utilizing 2 × 2 MIMO, 4 × 4 MIMO, and 8 × 8 MIMO, respectively. Moreover, while reducing channel spacing improves the system’s spectral efficiency from 1.6 to 6.4 bits/s/Hz, it also degrades the link range and requires an increased OSNR. A performance comparison between our work and related works is also considered in this paper.

Unsupervised Low-Light Image Enhancement via Virtual Diffraction Information in Frequency Domain

With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios. To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency-domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric “field of light”, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the “field of light” and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised methods, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have extremely fast inference speed (near 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600×400×3). Furthermore, the potential benefits of our method to object detection in the dark are discussed.

Numerical evaluation of bidirectional high-speed data transmission over turbulence tolerable FSO link employing WDM-OAM multiplexing and DP-QPSK modulation techniques

Orbital angular momentum (OAM) mode multiplexing is achieving utmost interest for the enhancement of transmission capacity and spectral efficiency. We have reported a (2 × 8 × 112 Gbps) 1792 Gbps bi-directional FSO link based on WDM-OAM multiplexing and dual-polarization quadrature phase-shift keying (DP-QPSK) modulation technique. An optical frequency comb lines (OFCL) generator, capable to generate 44 comb lines with flatness∼0 dB is employed as a broadband laser source. The efficiency of the system is not only checked for clear weather but also for different turbulence situations (weak, moderate, and strong atmospheric turbulence). According to the best of our knowledge, this is the first one where the effect of atmospheric turbulence on DP-QPSK data carrying OAM modes has been checked for a bidirectional link. The combination of coherent detection with digital signal processing is utilized for compensating wave-form distortion and noise cancellation due to atmospheric turbulence. A very low power penalty of 1.65 dB in clear weather and 4.91 dB in strong atmospheric turbulence at a BER∼10 −9 with open eye and clear constellation diagrams depicts the feasibility of the proposed system.

Microwave imaging sensor system using metamaterial lens for subwavelength resolution

Lenses made of negative refractive index metamaterials can overcome the diffraction-limited resolution of imaging at working distances in the far-field. This paper describes a novel negative index metamaterial (NIM) lens imaging sensor system for subwavelength microwave nondestructive evaluation (NDE) using homodyne detection measurements. The split-ring resonator (SRR) - wire based NIM lens is optimized to work at 6.3 GHz (wavelength λ = 48 mm). Coherent homodyne detection scheme provides a simple, low-cost, and highly sensitive NIM lens imaging sensor system that can be used in the field under practical conditions. Imaging of an omnidirectional source using negative refraction is shown using the proposed system. A focal spot of size 0.62λ was obtained in the image plane of the metamaterial lens with a source to image distance of 1.67λ. The focal spot position was further shifted in the azimuthal plane by simple movement of the source. The study demonstrates unique focus-scanning capability of NIM lenses for target localization without the need for complex tuning mechanisms. The feasibility of the system to image subwavelength defects inside dielectric materials in reflection mode is also investigated for its potential application as an NDE sensor. Defects comprising subwavelength hole of diameter 0.25λ and a groove of dimensions 0.31λ x 0.1λ placed at the focal plane of the lens was imaged using the proposed sensor system.

Polarization-Diversity-Based Rotation Sensing Methodology Using COTS UHF RFID Tags

Phase-based sensing using ultra high frequency (UHF) radio frequency identification (RFID) has, in recent years, yielded numerous additions to the Internet of Things (IoT). This work presents a polarization diversity based rotation sensing methodology using common-off-the-shelf (COTS) UHF RFID tags identified with a software defined radio (SDR) UHF RFID reader. The proposed methodology uses the tag-to-reader message after fully coherent demodulation to calculate a difference signal of the backscatter load modulation states. This sequence is then used to compute the rotation speed by evaluating its phase change over time. Experimental results are used to validate the theoretical model and to evaluate the performance and limitiations of the proposed system.

Theory of coherent interaction-free detection of pulses

Theory of coherent interaction-free detection of pulses

Fully distributed hydroacoustic sensing based on ultra-highly sensitive and lightweight fiber-optic hydrophone cable

In this paper, we demonstrate a fully distributed hydroacoustic sensing based on the ultra-highly sensitive and lightweight fiber-optic hydrophone cable assisted with heterodyne phase-sensitive optical time domain reflectometry. The proposed lightweight hydrophone cable is an all-solid state multilayer composite cable, which mainly consists of cable core, acoustic sensitization layer, sensing fiber, and protective layer. Based on the theoretical and simulation analysis results, the acoustic sensitization layer of the hydrophone cable is extruded from thermoplastic polyurethanes to enhance the acoustic sensitivity, and the sensing fiber is uniformly wound around the acoustic sensitization layer with pre-stress. To improve the signal-to-noise ratio of the sensing signal and guarantee the measurement stability in complex underwater environment, the backscattering enhanced optical fiber inscribed with a series of backscattering enhanced points is employed as the sensing fiber to suppress the coherent fading noise. Besides, the differential-polarization-vector-sum algorithm based on the polarization diversity reception is proposed and utilized to suppress the polarization fading noise of the coherent detection system. In the experiments, the results demonstrate an excellent acoustic sensitivity of −137.2 dB re:1 rad/(μPa·m) with a flat frequency response at the range of 5 Hz ∼ 2 kHz, which is consistent with the theoretical analysis and simulation result, as well as it can keep stable whether at the water pressure of 0.3 MPa or the axial elongation of 2%. Besides, it is noteworthy that the acoustic sensitivity is higher in the frequency range of 1 Hz ∼ 5 Hz, which even can reach up to −125.3 dB re:1 rad/(μPa·m) at 1 Hz. Lastly, the noise equivalent pressure of the proposed system is about 48 dB re:1μPa/Hz1/2 at 1 kHz, which is lower than the level of the Deep Sea State Zero. In field tests, the lightweight fiber-optic hydrophone cable can be rapidly deployed, and various emergency intrusive targets like boat, frogman, etc. are all detected and tracked based on the characteristics of high sensitivity and broadband response, which has exhibited the proposed system has tremendous potential for the future surface and underwater surveillance applications.

Attack Classification of Imbalanced Intrusion Data for IoT Network Using Ensemble-Learning-Based Deep Neural Network

With the increase in popularity of Internet of Things (IoT) and rise in interconnected devices, the need to foster effective security mechanism to handle vulnerabilities and risks in IoT networks has become evident. Security mechanisms such as Intrusion Detection System (IDS) are designed and deployed in IoT network environment to ensure security and prevent unauthorized access to system and resources. Moreover, there have been efforts to design IDS using various Deep Learning (DL) techniques, as these techniques possess intriguing characteristic of representing data with high abstraction. However, intrusion detection datasets used in literature possess imbalance class distribution, which is one of the challenging issue in developing coherent and potent intrusion detection and classification system. In this paper, we aim to address class imbalance problem using ensemble learning approach, namely, Bagging classifier, that uses Deep Neural Network (DNN) as base estimator. Here, in the proposed approach, the training process of DNN is influenced by including class weights that advocates to create balanced training subsets for DNN. The desirability and merit of the proposed approach can be considered as two-fold as it aims to achieve generalization along with addressing the class imbalance problem in intrusion detection datasets. The performance of the proposed approach is evaluated using four intrusion detection datasets, namely, NSL-KDD, UNSW_NB-15, CIC-IDS-2017, and BoT-IoT. Result analysis of the proposed approach is illustrated using various evaluation metrics, namely, accuracy, precision, recall, f-score, and False Positive Rate (FPR). Moreover, results of the proposed approach are also statistically tested using Wilcoxon signed-rank test.

A New Index Modulation for LoRa

LoRa (Long Range) is a widely adopted Internet-of-Things (IoT) technique, but its fatal limit is a low data rate. In this letter, we propose a new index modulation for LoRa to increase the data rate, in which multiple quasi-orthogonal chirps modulated under different spreading factors (SFs) are concurrently transmitted, thereby leveraging the SF domain as a means to carry more information. Both coherent and non-coherent detection algorithms for the proposed index modulation are also developed in efficient forms. Numerical results demonstrate that the proposed scheme notably outperforms the state-of-the-art techniques.

Ultra-wideband chaotic optical communication based on electro-optic differential feedback loop

A differential feedback electro-optical phase chaotic system is proposed to generate ultra-wideband optical chaos and realize high-speed long-distance chaotic communication. The nonlinear dynamics of this differential feedback loop are studied in detail. The numerical results show, firstly, in terms of chaos complexity, the highest permutation entropy of 0.993 and the largest spectral entropy of 0.945 are achieved. Regarding the key hiding, the time delay signature of phase chaos on both the autocorrelation function and delayed mutual information can be completely suppressed, so security can be guaranteed. Secondly, the maximum bandwidth of the radio spectrum is 46.75 GHz in the range of 50 GHz, and the minimum flatness is 1.62 dB/GHz. Finally, the reception of format-transparent messages can be realized by combining co-drive synchronization and coherent demodulation. The 50 GBaud QPSK signal can transmit 2400 km and the 25 GBaud 16 QAM signal can transmit 1500 km at an OSNR of 25 dB. This system realizes ultra-wideband high-complexity chaos and chaotic communication, which can greatly improve the security, transmission distance, and transmission rate of chaotic optical communication.

Massively parallel FMCW lidar with cm range resolution using an electro-optic frequency comb.

Frequency-modulated continuous wave (FMCW) light detection and ranging (lidar) is a promising solution for three-dimensional (3D) imaging and autonomous driving. This technique maps range and velocity measurement to frequency counting via coherent detection. Compared with single-channel FMCW lidar, multi-channel FMCW lidar can greatly improve the measurement rate. A chip-scale soliton micro-comb is currently used in FMCW lidar to enable multi-channel parallel ranging and significantly increase the measurement rate. However, its range resolution is limited due to the soliton comb having only a few-GHz frequency sweep bandwidth. To overcome this limitation, we propose using a cascaded modulator electro-optic (EO) frequency comb for massively parallel FMCW lidar. We demonstrate a 31-channel FMCW lidar with a bulk EO frequency comb and a 19-channel FMCW lidar using an integrated thin-film lithium niobate (TFLN) EO frequency comb. Both systems have a sweep bandwidth of up to 15 GHz for each channel, corresponding to a 1-cm range resolution. We also analyze the limiting factors of the sweep bandwidth in 3D imaging and perform 3D imaging for a specific target. The measurement rate achieved is over 12 megapixels per second, which verifies its feasibility for massively parallel ranging. Our approach has the potential to greatly benefit 3D imaging in fields where high range resolution is required, such as in criminal investigation and precision machining.

Image transmission performance analysis through free space optical communication link using coherent QPSK modulation under various environmental conditions

AbstractFree space optics (FSO) technology has been used recently in many applications due to its various advantages such as high bandwidth, easy deployment, high security, free installation license and immunity to electromagnetic interference. However, this type of communication is vulnerable to meteorological conditions such as rain, haze and fog which significantly degrade the system's efficiency. To enhance the effectiveness of the FSO systems, Quadrature Phase Shift Keying (QPSK) modulation based on coherent detection constitutes a promising technique for achieving high spectral efficiency and long‐distance FSO links with the characteristics of low‐cost implementation and simple design. This work aims to investigate the performance of FSO communication links on image transmission under the presence of atmospheric attenuations. A comparative study has been made using QPSK modulation scheme and on–off‐keying (OOK) modulation scheme to estimate the maximum range that was used to recover an RGB transmitted image over an FSO link under different meteorological weathers. The obtained results clearly show that the quality of the received image has degraded significantly during transmission through free space yielding to limit the propagation range. Moreover, the proposed FSO communication system performs well when using QPSK modulation rather than OOK modulation in different environmental conditions. Thus, to improve the quality of the received image under adverse fog weather condition, high power and small beam divergence angle have been suggested to increase system's performance. The proposed solution increases the communication range of the FSO link by 230 m and 100 m for moderate and heavy fog, respectively.

Fast adaptation of multi-task meta-learning for optical performance monitoring.

An algorithm is proposed for few-shot-learning (FSL) jointing modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) estimation. The constellation diagrams of six widely-used modulation formats over a wide range of OSNR (10-40 dB) are obtained by a dual-polarization (DP) coherent detection system at 32 GBaud. We introduce auxiliary task to model-agnostic meta-learning (MAML) which makes the gradient of meta tasks decline faster in the direction of optimal target. Ablation experiments including multi-task model-agnostic meta-learning (MT-MAML), single-task model-agnostic meta-learning (ST-MAML) and adaptive multi-task learning (AMTL) are executed to train a data set with only 20 examples for each class. First, we discuss the impact from the number of shots and gradient descent steps for support set on the meta-learning based schemes to determine the best hyper parameters and conclude that the proposed method better captures the similarity between new and previous knowledge at 4 shot and 1 step. Withdrawn fine-tuning, the model achieves the lowest error ∼0.37 dB initially. Then, we simulate two other schemes (AMTL and ST-MAML), and the numerical results shows that mean square error (MSE) are ∼0.6 dB, ∼0.3 dB and ∼0.18 dB, respectively, proposed method has faster adaption to main task. For low order modulation formats, the proposed method almost reduces the error to 0. Meanwhile, we reveal the degree of deviation between the prediction and target and find that the deviation is mainly concentrated in the high OSNR range of 25-40 dB. Specifically, we investigate the variation curve of adaptive weights during pretraining and conclude that after 30 epoch, the model's attention was almost entirely focused on estimating OSNR. In addition, we study the generalization ability of the model by varying the transmission distance. Importantly, excellent generalization is also experimentally verified. In this paper, the method proposed will greatly reduce the cost for repetitively collecting data and the training resources required for fine-tuning models when OPM devices need to be deployed at massive nodes in dynamic optical networks.

Remote terahertz spectroscopy from extended two-color plasma filaments: The ALTESSE 2 project

Coherent time-domain spectroscopy (TDS) using terahertz radiation is valuable for fundamental science, security, and medical applications. This study investigates the performance of air-biased coherent detection terahertz spectroscopy (ABCD-THz) when an extended plasma filament is created in the air over long distances. We report on the latest results obtained within the follow-up of the ALTESSE project (Bergé L. et al., EPL, 126 (2019) 24001) whose objective is to measure a set of spectral signatures characterizing suspicious materials over meter-long distances. As one of the most critical steps towards routinely applying this technique, we verified the feasibility of a remote THz time-domain spectroscopy by loosely focusing two-color ultrashort laser pulses at more than 3 meters from the laser source. The absorption spectra of amino acids and explosives analyzed in such a filamentation geometry are compared with those obtained using a standard ABCD scheme where the plasma is generated at much shorter distances of .

Monitoring seismicity in the southern Sichuan Basin using a machine learning workflow

Monitoring seismicity in real time provides significant benefits for timely earthquake warning and analyses. In this study, we propose an automatic workflow based on machine learning (ML) to monitor seismicity in the southern Sichuan Basin of China. This workflow includes coherent event detection, phase picking, and earthquake location using three-component data from a seismic network. By combining PhaseNet, we develop an ML-based earthquake location model called PhaseLoc, to conduct real-time monitoring of the local seismicity. The approach allows us to use synthetic samples covering the entire study area to train PhaseLoc, addressing the problems of insufficient data samples, imbalanced data distribution, and unreliable labels when training with observed data. We apply the trained model to observed data recorded in the southern Sichuan Basin, China, between September 2018 and March 2019. The results show that the average differences in latitude, longitude, and depth are 5.7 ​km, 6.1 ​km, and 2 ​km, respectively, compared to the reference catalog. PhaseLoc combines all available phase information to make fast and reliable predictions, even if only a few phases are detected and picked. The proposed workflow may help real-time seismic monitoring in other regions as well.

Effect of Wavefront Distortion in Non-Kolmogorov Turbulence on the Performance of Multi-Beam Transmission Coherent Detection

This study aims to address the multi-beam transmission problem of optical wireless coherent communication systems under the influence of non-Kolmogorov turbulence. This paper establishes the mathematical model for mixing efficiency and BER of non-Kolmogorov turbulence wavefront distortion and multi-beam transmission coherent detection systems. The influence of factors such as spectral power-law index, zenith angle, and transmission distance on the communication performance of the system is analyzed, and an experimental system is built using adaptive optics equipment to test the proposed theory. Numerical analyses show that in a non-Kolmogorov turbulence environment, as the spectral power-law index, zenith angle, transmission distance, Fresnel zone, and pointing error attenuation increase, the mixing efficiency of the coherent detection system decreases and the BER increases; under the same conditions, the mixing efficiency and BER of the system are improved after increasing the number of transmission beams at the transmitting end. Experimental research shows that, for the coherent detection system after wavefront correction using adaptive optics, the wavefront PV and RMS variances are 1.68 μm2 and 0.05 μm2 when a single beam of light is transmitted at the transmitting end, and the wavefront PV and RMS variances are 0.23 μm2 and 0.01 μm2 when two beams are transmitted. It is verified that using multi-beam transmission wavefront superposition to suppress wavefront distortion and wavefront jitter can effectively improve the mixing efficiency and BER of the coherent detection system at the receiving end.

Silicon Photonic Phase-Diverse Receiver Enabling Transmission of >Net 250 Gbps/λ Over 40 km for High-Speed and Low-Cost Short-Reach Optical Communications

The fast growth of data traffic in short-reach optical communications drives the demand for high-speed and low-cost optical modules. Here we demonstrate a silicon photonic (SiP) phase-diverse receiver based on our recently proposed asymmetric self-coherent detection (ASCD) scheme with Mach-Zehnder interferometers (MZI). The SiP MZI-ASCD receiver has a hardware-efficient architecture and recovers complex double-sideband signals via the beatings between a signal portion and a delayed signal portion from an asymmetric MZI using only 2 single-ended photodiodes and 2 analog-to-digital converters. The removal of the local oscillator in the direct detection receiver enables low-cost uncooled lasers, whereas the phase diversity closes the gap with coherent detection in terms of electrical spectral efficiency (ESE). Using a SiP MZI-ASCD receiver with a 15 ps delay, we detect a 60 Gbaud single-polarization 16-QAM signal transmitted over 40 km of single-mode fiber (SMF) below 20% overhead (OH) SD-FEC threshold of 2×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> with a net bit rate of 200 Gbps and a record net ESE of 5.86 b/s/Hz per wavelength per polarization. Using a SiP MZI-ASCD receiver with a different delay of 7.5 ps, transmission of single-polarization 16-QAM DSB signal up to 80 Gbaud over 40 km is achieved below 24% OH SD-FEC threshold of 4.5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> with a record net bit rate of 258 Gbps and a net ESE of 5.31 b/s/Hz per wavelength per polarization.

Physically Encrypted Wireless Transmission Based on XOR between Two Data in Terahertz Beams

Future wireless communications require higher security as well as a higher data rate. We have been studying physically secured wireless transmission systems and previously proposed encryption/decryption techniques based on the AND operation caused by coherent detection between two encrypted data sequences on two different terahertz carriers. Furthermore, we suggested that by employing the XOR operation as the decryption, the proposed system can be made more secure because XOR increases the computational complexity for eavesdroppers to recover the plaintext. In this paper, we propose the XOR operation between two data sequences on FSK-modulated terahertz waves. By constructing the XOR encryption transmitters/receivers, which consisted of high-speed wavelength tunable lasers and arrayed uni-traveling-carrier photodiodes (UTC-PDs), we successfully demonstrated the XOR operation between two data sequences on 200 GHz waves from the two transmitters.

Efficient Channel Estimation in Multiple Input Multiple Output Communication Systems using Hybrid Intelligence‐based Deep Learning Model using Horse Dingo Optimizer

AbstractThe estimation of the channel in multiple input multiple output (MIMO) systems over wireless communication has faced many challenges, such as high bit rate transmission with less time complexity, low error rate, and high convergence rate. And also, there is some potential for enhancing the system performance regarding channel efficiency diversity. MIMO system uses a huge count of antennas on the receiver and the transmitter side. This increases the cost requirements and power consumption. For achieving coherent detection in the MIMO system, optimization such as water filling and beam forming are required to find the proper channel. Hence, multiple transceivers used in both the transmitter side and receiver side create a channel estimation issue that is complicated when analyzed to a single input, single output system. Therefore, several solutions have been recommended to decrease the computational cost and power consumption in channel estimation of MIMO systems. This work intends to propose a novel deep structured architecture to handle the channel estimation problem in a MIMO system. The major objective of the developed method is to validate the channel coefficients of MIMO at the transmitter according to the obtained SNR feedback data packet from an acceptor with the help of the hybrid intelligence‐based deep learning model (HI‐DLM). For both scenarios, the HI‐DLM is introduced, in which the convolution neural network (CNN) is integrated with other deep‐structured architecture. Especially, the fully connected layers of CNN are restored by the long short‐term memory. In the HI‐DLM‐aided channel estimation, the hyperparameter optimization is accomplished by the novel dingo‐horse herd optimization for reducing the mean square error and bit error rate to acquire the estimated channel. The corresponding algorithm is validated on the communication of the MIMO system by experimentations. Thus, it is revealed that the offered algorithm provides better convergence and less computational cost.

Millimeter‐Wave WISP Search with Coherent Light‐Shining‐Through‐a‐Wall Toward the STAX Project

AbstractA dark photon is one of the simplest extensions of the Standard Model of particle physics and can be a dark matter candidate. Dark photons kinetically mix with ordinary photons. The mass range from 10−4to 10−3 eV of such dark photons is underconstrained by laboratory‐based experiments and a new search is therefore motivated. In this mass range, dark photons behave like waves rather than particles and the corresponding electromagnetic waves are in the millimeter‐wave range. The technical difficulties of the millimeter waves have prevented so far dark photon experiments in this mass range. The use of coherent millimeter waves to search for dark photons in a Light‐Shining‐through‐a‐Wall (LSW) experiment is proposed. The merits and limitations of coherent wave detection are clarified and the potential of single photon sensors at microwaves is investigated. Development of millimeter‐wave technology is not only limited to dark photons. Technically, an experiment for dark photons by using electromagnetic waves resembles that for axions, another light dark matter candidate, with static magnetic fields. This paper represents an essential step toward axion LSW in the millimeter‐wave range (Sub‐THz‐AXion experiment; STAX) as a potential successor of an on‐going experiment in infrared.