Underwater Visible Light Communication System Research Articles

Modulation format recognition in a UVLC system based on an ultra-lightweight model with communication-informed knowledge distillation

Modulation format recognition (MFR) is a key technology for adaptive optical systems, but it faces significant challenges in underwater visible light communication (UVLC) due to the complex channel environment. Recent advances in deep learning have enabled remarkable achievements in image recognition, owing to the powerful feature extraction of neural networks (NN). However, the high computational complexity of NN limits their practicality in UVLC systems. This paper proposes a communication-informed knowledge distillation (CIKD) method that achieves high-precision and low-latency MFR with an ultra-lightweight student model. The student model consists of only one linear dense layer under a communication-informed auxiliary system and is trained under the guidance of a high-complexity and high-precision teacher model. The MFR task involves eight modulation formats: PAM4, QPSK, 8QAM-CIR, 8QAM-DIA, 16QAM, 16APSK, 32QAM, and 32APSK. Experimental results show that the student model based on CIKD can achieve comparable accuracy to the teacher model. After knowledge transfer, the prediction accuracy of the student model can be increased by up to 87%. Besides, it is worth noting that CIKD’s inference accuracy can reach up to 100%. Moreover, the parameters constituting the student model in CIKD correspond to merely 18% of the parameters found in the teacher model, which facilitates the hardware deployment and online data processing of MFR algorithms in UVLC systems.

Neural-network-based end-to-end learning for adaptive optimization of two-dimensional signal generation in UVLC systems.

Visible light communication (VLC) benefits from the underwater blue-green window and holds immense potential for underwater wireless communication. In order to address the limitations of various equipment and harsh channel conditions in the underwater visible light communication (UVLC) system, the researchers proposed to use the method of autoencoder (AE) to tap the potential of the system. However, traditional AE schemes involve replacing the transmitting and receiving components of a communication system with a large multilayer perceptron (MLP) network, and they have significant drawbacks due to their reliance on a single network structure. In this paper, a novel 2D adaptive optimization autoencoder (2D-AOAE) framework is proposed to realize adaptive modulation and demodulation of two-dimensional signals. By implementing this scheme, we experimentally achieved a transmission rate of 2.85 Gbps over a 1.2-meter underwater VLC link. Compared to the traditional 32QAM UVLC system, the 2D-AOAE scheme demonstrated a 15.4% data rate increase. Moreover, the 2D-AOAE scheme exhibited a remarkable 73% improvement when compared to the UVLC system utilizing the traditional AE scheme. This significant enhancement highlights the superior performance and capabilities of the 2D-AOAE scheme in terms of transmission rate.

Performance analysis of convolutional codes in dynamic underwater visible light communication systems

Performance analysis of convolutional codes in dynamic underwater visible light communication systems

An Optimal Adaptive Constellation Design Utilizing an Autoencoder-Based Geometric Shaping Model Framework

Since visible-light communication (VLC) has become an increasingly promising candidate for 6G, the field of underwater visible-light communication (UVLC) has also garnered significant attention. However, the impairments introduced by practical systems and the time-varying underwater channels always limit the performance of underwater visible-light communication. In this paper, we propose and experimentally demonstrate an autoencoder-based geometric shaping model (AEGSM) framework to jointly optimize quadrature amplitude modulation (QAM) signals at the symbol-wise and bit-wise levels for underwater visible-light communication. Unlike traditional geometric shaping (GS) methods, which only give theoretically optimal shaping solutions, our framework can always obtain the globally optimal shaping scheme for a specific channel condition or different application scenarios. In our AEGSM framework, an autoencoder is used to find the optimal shaping scheme at the symbol-wise level and a revised pairwise optimization (RPO) algorithm is applied to achieve bit-wise optimization. In a real UVLC system, 2.05 Gbps transmission is achieved under the hard decision–forward error correction (HD-FEC) threshold of 3.8 × 10−3 by employing the autoencoder-based 8QAM (AE-8QAM) optimized by the AEGSM, which is 103 Mbps faster than the Norm-8QAM. The AE-8QAM also shows its resistance to nonlinearity and enables the UVLC system to operate within a larger dynamic range of driving voltages. The results substantiate the potential and practicality of the proposed AEGSM framework in the realm of underwater visible-light communication.

Adaptive Fuzzy Logic Deep-Learning Equalizer for Mitigating Linear and Nonlinear Distortions in Underwater Visible Light Communication Systems

Underwater visible light communication (UVLC) has recently come to light as a viable wireless carrier for signal transmission in risky, uncharted, and delicate aquatic environments like seas. Despite the potential of UVLC as a green, clean, and safe alternative to conventional communication methods, it is challenged by significant signal attenuation and turbulent channel conditions compared to long-distance terrestrial communication. To address linear and nonlinear impairments in UVLC systems, this paper presents an adaptive fuzzy logic deep-learning equalizer (AFL-DLE) for 64 Quadrature Amplitude Modulation-Component minimal Amplitude Phase shift (QAM-CAP)-modulated UVLC systems. The proposed AFL-DLE is dependent on complex-valued neural networks and constellation partitioning schemes and utilizes the Enhanced Chaotic Sparrow Search Optimization Algorithm (ECSSOA) to improve overall system performance. Experimental outcomes demonstrate that the suggested equalizer achieves significant reductions in bit error rate (55%), distortion rate (45%), computational complexity (48%), and computation cost (75%) while maintaining a high transmission rate (99%). This approach enables the development of high-speed UVLC systems capable of processing data online, thereby advancing state-of-the-art underwater communication.

SIMO-Underwater Visible Light Communication (UVLC) system

The advent of visible light communication (VLC) towards 5G and beyond (5GB) networks opened new doors to unveil the mysteries of unguided mediums such as ocean. The varying physicochemical properties of water introduces the underwater optical turbulence (UOT). The UOT of UVLC link is modelled as unified Gamma-Gamma (GG) distribution fading under moderate-to-strong turbulence channel conditions as well as the misalignment of transceivers following exponential fading are included and studied. This study also contains the effect of UOT which is characterized by multiple water layers within the Monte Carlo simulation framework. The end-to-end (E2E) performances of a Single-Input and Multi-Output (SIMO) underwater VLC (UVLC) system is in consideration of Intensity-Modulation/Direct-Detection (IM/DD) technique which is obtained to fulfill the current communication requirements in hostile aqueous channels. More specifically, the probability distribution function (PDF) and cumulative distribution function (CDF) are derived to obtain the E2E performance metrics of the SIMO-UVLC system in terms of Meijer-G function. Consequently, novel closed-form expressions for average bit-error-rate (ABER) and outage probability of the system are formulated. Finally, the numerical and simulation results are obtained based on the experimental data in mixed water layers of the oceanic environments, and analytically verified through the Monte Carlo simulation approach in harsh channel conditions.

Modulation format recognition in a UVLC system based on reservoir computing with coordinate transformation and folding algorithm.

Modulation format recognition (MFR) is one of the key technologies in adaptive optical systems and widely used in both commercial and civil applications. With the rapid development of deep learning, MFR algorithm based on neural networks (NN) has achieved impressive success. Due to the high complexity of underwater channels, to gain better performance of MFR tasks in underwater visible light communication (UVLC), the NN tend to be designed with a complex structure, which is costly in computation and hinders fast allocation and real-time processing. In this paper, we propose a lightweight and efficient method based on reservoir computing (RC), whose trainable parameters are only 0.3% of common NN-based methods. To improve the performance of RC in MFR tasks, we propose powerful feature extraction algorithms including coordinate transformation and folding algorithm. The proposed RC-based methods are implemented for six modulation formats, including OOK, 4QAM, 8QAM-DIA, 8QAM-CIR, 16APSK, and 16QAM. The experimental results show that our RC-based methods take only a few seconds for training process and under different pin voltages of LED, the accuracy for almost all exceeds 90%, and the highest is close to 100%. Analysis on how to design a well-performed RC to strike a balance between accuracy and time cost is also investigated, providing a useful guide for RC implementations in MFR.

Enhancing underwater VLC with spatial division transmission and pairwise coding.

In this paper, we propose and evaluate two spatial division transmission (SDT) schemes, including spatial division diversity (SDD) and spatial division multiplexing (SDM), for underwater visible light communication (UVLC) systems. Moreover, three pairwise coding (PWC) schemes, including two one-dimensional PWC (1D-PWC) schemes, i.e., subcarrier PWC (SC-PWC) and spatial channel PWC (SCH-PWC), and one two-dimensional PWC (2D-PWC) scheme are further applied for signal-to-noise ratio (SNR) imbalance mitigation in the UVLC systems using SDD and SDM with orthogonal frequency division multiplexing (OFDM) modulation. The feasibility and superiority of applying SDD and SDM with various PWC schemes in a practical bandlimited two-channel OFDM-based UVLC system have been verified through both numerical simulations and hardware experiments. The obtained results show that the performance of SDD and SDM schemes are largely determined by both the overall SNR imbalance and the system spectral efficiency. Moreover, the experimental results demonstrate the robustness of SDM with 2D-PWC against bubble turbulence. Specifically, SDM with 2D-PWC can obtain bit error rates (BERs) under the 7% forward error correction (FEC) coding limit of 3.8 × 10-3 with a probability higher than 96% for a signal bandwidth of 70 MHz and a spectral efficiency of 8 bits/s/Hz, achieving an overall data rate of 560 Mbits/s.

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.

Implementation of MIMO concept for underwater visible light communication system

Implementation of MIMO concept for underwater visible light communication system

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.

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.

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.

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

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.

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.