Underwater Visible Light Communication Research Articles

Physical prior inspired ensemble learning enables effective channel estimation of underwater visible light communication.

Deep neural networks have been applied to estimate the optical channel in communication systems. However, the underwater visible light channel is highly complex, making it challenging for a single network to accurately capture all its features. This paper presents a novel approach to underwater visible light channel estimation using a physical prior inspired network based on ensemble learning. A three-subnetwork architecture was developed to estimate the linear distortion from inter-symbol interference (ISI), quadratic distortion from signal-to-signal beat interference (SSBI), and higher-order distortion from the optoelectronic device. The superiority of the Ensemble estimator is demonstrated from both the time and frequency domains. In terms of mean square error performance, the Ensemble estimator outperforms the LMS estimator by 6.8 dB and the single network estimators by 15.4 dB. In terms of spectrum mismatch, the Ensemble estimator has the lowest average channel response error, which is 0.32 dB, compared to 0.81 dB for LMS estimator, 0.97 dB for the Linear estimator, and 0.76 dB for the ReLU estimator. Additionally, the Ensemble estimator was able to learn the V-shaped Vpp-BER curves of the channel, a task not achievable by single network estimators. Therefore, the proposed Ensemble estimator is a valuable tool for underwater visible light channel estimation, with potential applications in post-equalization, pre-equalization, and end-to-end communication.

On Performance Analysis of Cooperative Horizontal and Vertical Underwater VLC Systems with Best Relay Selection

In this paper, we consider cooperative horizontal and vertical underwater visible light communication (UWVLC) systems employing best relay selection. In the vertical UWVLC system, the source is placed at the sea surface and the destination is placed at a depth of 60 m. The link between the source and the destination is modeled as concatenated layers considering inhomogeneous environmental conditions. The underwater parameters change with depth, causing a variable refractive index, which leads to non-uniform optical turbulence. The horizontal communication link is analyzed for two different levels of turbulence at 10 m and 50 m depths. Turbulence is modeled using a log-normal distribution, whose statistical parameters depend on the temperature and salinity at different depths. We have also taken into account the path losses caused by absorption and scattering, and carried out a comprehensive performance comparison between the horizontal and the vertical UWVLC systems. Insights show that compared to horizontal links, vertical links need an extra relay in order to achieve the same level of matching performance. Moreover, it is shown that the horizontal system’s outage performance improves with depth.

Effect of Sea Waves on Vertical Underwater Visible Light Communication Links

Underwater visible light communication (VLC) has been proposed to deal with emerging high bandwidth underwater applications. Initial research works on underwater VLC are based on the assumption that both transmitter and receiver are submerged, creating a horizontal link. In most of the vertical communication links, one of the transceiver nodes takes the form of a buoy and requires taking into the effect of the sea surface, which is inherently unsteady due to wind and waves. In this article, we consider a vertical underwater VLC link where the transmitter is in the form of a buoy at the sea surface, and the receiver is a submerged node at a certain depth. We assume sinusoidal waves and consider the fact that the buoy will fluctuate and oscillate, during drifting up and down, around its vertical axis. This effectively results in a 3-D displacement at the suspended transmitter. Building upon these assumptions of practical relevance, we propose an aggregate channel model, which includes a random path loss due to periodic changes of the transmission distance and a fading term induced by pointing errors with periodic changes of relative movement. Based on the proposed statistical model, we derive closed-form expressions for the exact and asymptotic bit error ratio and investigate the achievable diversity orders. We further present numerical results to confirm the analytical findings.

Optimal Adaptive Waveform Design Utilizing an End-to-End Learning-Based Pre-Equalization Neural Network in an UVLC System

Visible light communication (VLC) has emerged as a promising communication method in the special field of 6G, such as intersatellite communication (ISC) and underwater wireless optical communication (UWOC). Green light-emitting diode (LED)-based underwater visible light communication (VLC) is considered a potential candidate for UWOC of mobile autonomous underwater vehicle (AUV) and remotely operated underwater vehicle, particularly in coastal and harbor environments. However, due to bandwidth limitations and variable underwater channels, implementing high-speed underwater VLC remains a challenge. In this paper, a neural network-based auto equalization model (NNAEM) using end-to-end learning is proposed to achieve a high-speed underwater VLC link with a bandwidth-limited green LED. The entire NNAEM includes a neural network-based channel model, a pre-equalization neural network, and a post-equalization neural network. Based on the data-driven channel model, our auto equalization (AE) method can dynamically pre-equalize the discrete multitone (DMT) modulated signal in different bandwidth-limited cases and various nonlinearity cases. An underwater VLC link with a data rate of 3.451 Gbps and a transmission distance of 1.2 m is experimentally demonstrated by employing the proposed NNAEM under the hard decision-forward error correction (HD-FEC) threshold of 3.8 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> . The data rate improvement is up to 30.4% compared to other advanced digital signal processing (DSP) methods. The scenario adaptation and robustness of the NNAEM are also verified in the experiments, which demonstrate that the NNAEM can serve an important role in underwater high-speed VLC transmission.

Implementation of MIMO concept for underwater visible light communication system

Implementation of MIMO concept for underwater visible light communication system

Underwater VLC channel estimation based on Kalman filtering for direct current optical- and asymmetrically clipping optical- orthogonal frequency division multiplexing techniques

In this paper, we suppose an underwater optical wireless communication (UOWC) system, where the communication utilizes visible light communication for its advantages as wide spectrum, high data rate and high accuracy. The novelty of this paper is focused on improving the channel estimation between transmitter and receiver, where using Kalman Filter (KF) for channel estimation in UOWCs achieves the best results as compared to other traditional channel estimation methods. The scenario of this paper is summarized in transmitting data from transmitter to receiver via underwater harbor and coastal channels. Two channel models are utilized: weighted double gamma functions (WDGF) and a combination of exponential and arbitrary power function (CEAPF). The modulation technique used is optical orthogonal frequency division multiplexing with two kinds: direct current optical orthogonal frequency division multiplexing (DCO-OFDM) and asymmetrically clipping optical orthogonal frequency division multiplexing (ACO-OFDM). Three different techniques are used for channel estimation: Least Square (LS), minimum mean square error (MMSE), and KF. The simulation results reveal that the ACO-OFDM modulation technique with CEAPF channel modeling using KF achieves the lowest bit error rate (BER) compared to other channel estimation methods. The improvement percentage at BER = 10−1 is 13.3% for ACO-OFDM over DCO-OFDM with CEAPF in coastal water and is is 9.3% for WDGF. This indicates that CEAPF performs about 4% better than WDGF for ACO-OFDM than DCO-OFDM in terms of channel estimation.

Channel Polarization Scheme for Ocean Turbulence Channels in Underwater Visible Light Communication

Underwater wireless communication technology plays an important role in marine environment monitoring and ecological protection. Underwater optical wireless communications (UWOCs) can currently achieve a transmission distance of hundreds of meters, and the rate can reach hundreds of Mbps or even Gbps, with low power consumption and high-speed features. In addition, UWOC also has the advantages of a small transceiver size and strong anti-electromagnetic interference ability, which is especially suitable for scenarios where underwater volume and power consumption are relatively limited. However, UWOC systems face problems such as unstable transceiver ends, ocean turbulence, and so on, resulting in reduced communication reliability and limited transmission distance. Establishing a stable and reliable communication link is critical to extending the communication distance of the UWOC system. In this paper, a model of ocean turbulence channels is established based on the power spectrum inversion method. The transmission characteristics of orbital angular momentum (OAM) light in an ocean turbulence channel are studied, then the mode selection of OAM light is determined. At the same time, the polarization coding technique is applied to the underwater OAM communication system for the first time. The simulation results show that this scheme can effectively extend the communication distance and reduce the system bit error rate.

Enhanced Performance of a Cascaded Receiver Consisting of a DNN-Based Waveform-to-Symbol Converter and Modified NN-Based DD-LMS in CAP Underwater VLC System

Underwater visible light communication (UVLC) based on LEDs has become a competitive candidate, which is able to provide high data rates, low latency and low cost for next-generation wireless communication technologies. However, it is still challenging to achieve high-speed communication because of bottleneck problems such as bandwidth limitation and linear and nonlinear distortions. Traditional Deep-learning Neural Network (DNN)-based waveform-to-symbol converter is verified to be an effective method to alleviate them, but impractical due to high complexity. To achieve a better tradeoff between communication performance and computation complexity, a cascaded receiver consisting of a DNN-based waveform-to-symbol converter and modified Neural Network (NN)-based decision-directed least mean square (DD-LMS) is then innovatively proposed. With fewer taps and nodes than the traditional converter, the front-stage converter could mitigate the majority of Inter-Symbol Interference (ISI) and signal nonlinear distortions. Then modified NN-based DD-LMS is cascaded to improve communication performance by reducing phase offset, making received constellation points more concentrated and closer to standard constellation points. Compared with the traditional converter, the cascaded receiver could achieve 89.6% of signal Vpp dynamic range with 12.4% of complexity in the 64APSK UVLC system. Moreover, the ratio of signal Vpp dynamic range and total trainable parameters is 1.24 × 10−1 mV, while that of the traditional converter is 1.95 × 10−2 mV. The cascaded receiver used in 64APSK UVLC systems is experimentally verified to achieve enhanced performance, thus as a promising scheme for future high-speed underwater VLC.

Optimized deep learning/kalman filter-based underwater localization in VLC systems

Several applications depend on the localization technique in underwater visible light communication (UVLC) systems, as military, petroleum, and diving fields. Recent research aims to develop the localization system by different methods to obtain the optimum position of the receiver. In this paper, we use Kalman Filter (KF) algorithm with average Received Signal Strength (RSS) technique using optimization. Optimized Deep Learning Models (DLMs) are utilized to improve the system performance, including such as ResNet50V2, InceptionResNetV2, SSD, and RetinaNet. Two channel modeling Weighted Double Gamma Function (WDGF) with a Combination Exponential Arbitrary Power Function (CEAPF) are used for sea water to enhance the UVLC localization system. The obtained results show that using CEAPF channel modeling with ResNetV2 strategy achieves the best accuracy of the localization for different methods. Also, the ResNetV2 outperforms other strategies for using RSS average technique. The RSS with KF and DLM achieves a higher accuracy with ResNetV2 than InceptionResNetV2, RetinaNet and SSD. Using WDGF achieves accuracy less than that in CEAPF where for using KF with average RSS method. Applying the RSS with KF with CEAPF channel modeling improves the performance than using WDGF. We use an automatic hyper-parameter (HP) approach to the Bayesian optimization models ResNet50V2, InceptionResNetV2, SSD, and RetinaNet. The ResNet50V2 based on average RSS technique hybrid with KF in CEAPF channel model achieves 99.99% accuracy, 99.99% area under the curve (AUC), 99.98% precision, 99.89% F1-score, 0.099 RMSE and 0.43 s testing time.

Deep learning/Kalman filter-based underwater localization in VLC systems

There is a huge importance for the localization system in underwater visible light communication (VLC) systems as in petroleum, military and diving fields. To enhance the localization system, we use the Kalman filter (KF) algorithm with average received signal strength (RSS) method to obtain the nearest estimated positions. In this paper, two channel modeling weighted double Gamma functions (WDGF) are applied and a combination exponential arbitrary power function (CEAPF) for enhancing localization in VLC underwater systems. Using the proposed KF enhances the localization by ~ 60% as compared to the than average RSS technique for WDGF channel modeling and ~ 78% for the CEAPF channel modeling. Based on the estimate of received signal strength (RSS) by deep learning models (DLMs), underwater localization utilizing VLC is introduced. Our proposed framework is categorized into two phases. First, data collection is collected based on MATLAB software. Second, the training and testing of DLMs, SSD, RetinaNet, ResNet50V2 and InceptionResNetV2 techniques are applied. The channel gains are the DLMs’ input data set, while the DLMs’ output is the RSS intensity technique coordinates for each detector. The DLMs are then developed and trained using Python software. The ResNet50V2 based on average RSS technique hybrid with KF in CEAPF channel model achieves 99.98% accuracy, 99.97% area under the curve, 98.99% precision, 98.88% F1-score, 0.101 RMSE and 0.32 s testing time.

Security enhanced underwater visible light communication system based on chaotic phase scrambling and conjugate frequency hopping

We propose an encryption technique for underwater visible light communication (UVLC) based on chaotic phase scrambling (PS) and conjugate frequency hopping (CFH). The technique is experimentally tested using an 8-level pulse amplitude modulation (PAM-8) and a 1.2 m underwater link. The security key of the phase scrambling code is generated according to a logistic map, and the frequency hopping is achieved by adding the same zero frequency points to the signal spectrum. The maximum transmission rate of 2.1 Gbit/s is measured with bit-error-rate (BER) below 7% the hard-decision forward error correction (HD-FEC) threshold of 3.8×10-3.

Application of channel multiplexing technology in LED underwater visible light communication

Application of channel multiplexing technology in LED underwater visible light communication

Two-dimensional power allocation scheme for NOMA-based underwater visible light communication systems.

In this work, a two-dimensional power allocation scheme combining fractional transmit power allocation (FTPA) and the sine cosine algorithm (SCA) is proposed for non-orthogonal multiple access (NOMA)-based underwater visible light communication (UVLC) systems. Considering the proposed power allocation scheme, a downlink NOMA-based UVLC system using blue-light-emitting diodes in the deep-sea environment is set up to evaluate the influence of the FTPA coefficient and SCA on system communication performance. The simulation results demonstrate that the two-dimensional power allocation scheme can effectively reduce the impact of user pairing on system performance and improve the system transmission rate compared with the conventional power allocation scheme.

Transfer Learning Strategy in Neural Network Application for Underwater Visible Light Communication System

Post-equalization using neural network (NN) is a promising technique that models and offsets the nonlinear distortion in visible light communication (VLC) channels, which is recognized as an essential component in the incoming 6G era. NN post-equalizer is good at modeling complex channel effects without previously knowing the law of physics during the transmission. However, the trained NN might be weak in generalization, and thus consumes considerable computation in retraining new models for different channel conditions. In this paper, we studied transfer learning strategy, growing DNN models from a well-trained 'stem model' instead of exhaustively training multiple models from randomly initialized states. It extracts the main feature of the channel first whose signal power balances the signal-to-noise ratio and the nonlinearity, and later focuses on the detailed difference in other channel conditions. Compared with the exhaustive training strategy, stem-originated DNN models achieve 64% of the working range with five times the training efficiency at most or more than 95% of the working range with 150% higher efficiency. This finding is beneficial to improving the feasibility of DNN application in real-world UVLC systems.

Geometrically Shaped 32QAM and Modified Binary Switching Coding Method in Underwater Visible Light Communication

Geometrically Shaped 32QAM and Modified Binary Switching Coding Method in Underwater Visible Light Communication

Support Vector Machine-Based Soft Decision for Consecutive-Symbol-Expanded 4-Dimensional Constellation in Underwater Visible Light Communication System

Nowadays, underwater visible light communication (UVLC) has become one of the key technologies for high-speed underwater wireless communication. Because of the limited modulation bandwidth and nonlinearity of the optoelectronic devices in the UVLC system, the combination of inter-symbol interference and nonlinear impairment will inevitably degrade the transmission performance. Advanced digital signal processing methods including equalization and decoding are required. In the past few years, Support vector machine (SVM) has been widely investigated in quadrature amplitude modulation (QAM) for soft decision in the decoding process. However, previous works only consider 2-dimensional (2-D) separate symbol, ignoring the correlation between consecutive symbols. In this paper, we propose to use SVM for soft decision with a 4-dimensional (4-D) constellation by concatenating two consecutive symbols. To deal with the increasing computational complexity in the SVM training phase, bit-based binary SVM multi-class strategy and an edge-detection-based data pre-processing method are employed. In this paper, we demonstrate a carrierless amplitude and phase (CAP) 16-QAM UVLC system. Experimental results indicate that the performance is greatly improved when using consecutive-symbol-expanded 4-D constellation with SVM for soft decision.

Reflector-aided underwater optical channel modeling.

Underwater visible light communication (UVLC) has been proposed as a high-speed alternative to acoustic signaling. While most UVLC systems are configured to work in line-of-sight (LOS) conditions, it is also possible to exploit reflected signals for performance enhancements. In this Letter, we propose a closed-form expression for the underwater path loss assuming non-LOS (NLOS) transmission through the water surface and man-made reflector (e.g., mirror) in addition to the LOS link. Utilizing the derived expression, we quantify the achievable NLOS gain defined as the ratio between the maximum achievable channel coefficient from reflection and the overall channel coefficient. We validate our findings experimentally by utilizing the water surface and the mirror as the reflecting surfaces in an aquarium. Our results reveal that achievable gains up to around 3 dB can be observed due to reflections.

Effect of transmitter divergence-angle on the performance of underwater visible light communication system

Effect of transmitter divergence-angle on the performance of underwater visible light communication system

Comparison of solar cell and photodiode performance for underwater visible light communications system with RF transmission by various techniques

Abstract The purpose of this paper is to design an experimental system for underwater wireless optical communications (UWOC). By using an amplitude shift key (ASK), different frequencies in KHz are modulated over LED light. Four strategies are evaluated and compared (SISO, SIMO, MISO, and MIMO) with the purpose of determining the success of receiving optical signals. To receive these frequencies, a solar cell and photodiode (BPX61) are utilized. A digital storage oscilloscope (DSO) is used to view the received signal at the identical optical conditions. The results indicate that the system has a satisfactory link quality to receive frequencies when all four approaches are used in the same settings. The PD BPX61 is more efficient at receiving frequencies from the solar cell. The measurable frequency range of the received signals was 500 KHz for the PD BPX61 and 100 KHz for the solar cell. The (SISO) technique achieves the lowest SNR values, whereas the MIMO technique achieves the highest values.

Efficient Modulation Classification Based on Complementary Folding Algorithm in UVLC System

Modulation classification (MC) has become a widely used technology, which is of great value in both commercial and civil applications. It actually completes the classification task of modulation signal through various means. In recent years, modulation format recognition based on deep learning (DL) has achieved great success. However, in practical application, the computational cost and model complexity have become the biggest obstacles of the traditional MC based on DL. To solve this problem, we propose complementary folding algorithm (CFA). This is an algorithm based on classical modulation classification (CMC), which folds and splices the features of the input neural network (NN), so that these features have both large-scale and small-scale dual branch receptive fields. The research results prove that under the same network structure and data quantity, both the correctness rate and the convergence speed of CFA are significantly improved in the communication experiment based on underwater visible light communication (UVLC). It is also worth mentioning that because of the particularity and complexity of the channel, UVLC system can be divided into different regions. In any region, CFA performs better than CMC, so we can prove that this algorithm also has excellent robustness.