Enabling technologies for high-speed LED based underwater visible light communications

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.

In visible light communication (VLC) system, the Orthogonal Frequency Division Multiplexing (OFDM) technique has been widely used to achieve a high data rate transmission. However, OFDM will cause high peak-to-average power ratio (PAPR) in the VLC system. In this paper, we propose and experimentally demonstrate a new interleaved Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing (DFT-S OFDM) method. We compare the interleaved DFT-S technique to other PAPR reduction techniques, such as Clipping OFDM and conventional DFT-S. The experimental results show that interleaved DFT-S achieve FEC threshold for an overall 2.0Gb/s 32QAM signal over the 1.05m transmission. In addition, interleaved DFT-S presents significant PAPR reduction, low complexity and optimal BER performance, when compared to other techniques.

Metro network, as a medium distance transmission system, poses a special challenge of transmission capacity and cost. In this paper, we present a detailed comparison of applying three advanced modulation formats including pulse amplitude modulation-4 (PAM-4), carrierless amplitude and phase modulation-16 QAM (CAP-16), and discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S OFDM) with low-cost direct detection for 100-Gb/s/λ metro optical transmission systems. These modulation formats are all experimentally demonstrated with similar digital signal processing algorithms. Three kinds of chromatic dispersion (CD) compensation ways named CD precompensation method, single sideband (SSB), and dispersion compensating fiber (DCF) are also compared. Two types of modulators including an IQ modulator and a dual-drive Mach–Zehnder modulator are employed to generate SSB or CD precompensated signals in this experiment and their transmission performance is also evaluated. To the best of our knowledge, this is the first time that the performance of single-wavelength 100-G signal transmission based on direct detection is evaluated over 480 km of standard single mode fiber with PAM-4, CAP-16, and DFT-S OFDM modulation formats.

In this article, we demonstrate two transfer learning–based dual-branch multilayer perceptron post-equalizers (TL-DBMLPs) in carrierless amplitude and phase (CAP) modulation-based underwater visible light communication (UVLC) system. The transfer learning algorithm could reduce the dependence of artificial neural networks (ANN)–based post-equalizer on big data and extended training cycles. Compared with DBMLP, the TL-DBMLP is more robust to the jitter of the bias current (Ibias) of light-emitting diode (LED), which indicates that TL-DBMLP does not require further training in Ibias varying UVLC system. In terms of voltage peak-to-peak (Vpp) varying VLC system, DBMLP requires a training set with a size of more than 105 and 50 training epochs, which quantitatively prove the effectiveness of DBMLP in reducing reliance on large amount of training epochs. On the counterpart, the TL-DBMLP only requires a training set with a size of less than 2×104 and 10 training epochs, which quantitatively prove the effectiveness of DBMLP in reducing reliance on big data. Finally, we experimentally demonstrate that transfer learning can effectively reduce ANN dependence on extensive size training data and large amount of training epochs, whether in VLC systems with varying Ibias and varying Vpp.

In this letter, we jointly consider the problems of blind channel estimation (BCE), channel equalization and data detection in underwater visible light communication (UVLC) systems. Specifically, we propose to solve them with a model-driven deep learning (DL) approach. Inspired by the module-based signal processing methodology and communication theory, we devise a new blind receiver architecture called blind detection network (BDNet), which consists of three modules corresponding to BCE, channel equalization, and symbol demapping, respectively. Different from existing BCE schemes, the BDNet does not directly estimate the channel itself, but instead estimates the inverse channel regardless of the scalar ambiguity issue by learning the latent channel features from the received signal only. The UVLC channel effects can then be partially compensated by the zero-forcing equalization module of BDNet. Finally, the symbol demapping module recovers the transmitted symbols with the aid of the learned decision thresholds. Simulation results show the competitive bit error rate performance of BDNet in comparison to conventional pilot-aided schemes.

In this paper, we compare the performance of three different digital-signal-processing-based directly detected 100 Gb/s transmission schemes, namely, optical orthogonal frequency division multiplexing, optical single-carrier frequency domain equalization, and optical discrete Fourier transform spread orthogonal frequency division multiplexing. The performances of these systems are characterized with different link parameters, including laser linewidth, modulation formats, RF frequency, modulator drive power, and so on. Simulation results suggest that at optimum operating conditions, receivers' sensitivities of these systems may not exhibit any significant difference with respect to each other, confirming their suitability for next-generation 100 Gb/s short-reach optical links.

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.

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.

We experimentally demonstrate 208-Gb/s/λ discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S OFDM) transmission over a 40-km standard single-mode fiber without chromatic dispersion compensation using a low-cost direct-detection architecture based on the dual-driver Mach-Zehnder modulator (MZM). One twin-single sideband (SSB) signal is generated by driving the upper and lower arms of a dual-driver MZM with a real signal and its respective Hilbert pair. A MIMO-Volterra equalizer is proposed to mitigate the interference and nonlinearity penalty of the twinSSB signal. Using the Twin-SSB without the MIMO algorithm can only reach 165 Gb/s/λ, and applying a conventional SSB can only achieve 164 Gb/s/λ with a bit error ratio less than 5×10 -3 . However, we achieve a 240-Gb/s/λ twin-SSB DFT-S OFDM signal in the back-to-back (BTB) case using the twin-SSB signal and MIMO-Volterra Equalization method, which implies that double spectral efficiency and more than 45% data rate improvement are obtained.

Ongoing advances in the research and development of visible light communication (VLC) systems has opened up many opportunities for using the concept of light based communication in underwater medium. Water presents a favourable medium for communication of optical wavelengths, particularly in the blue-violet regions of the visible spectrum and parts of ultraviolet wavelength. Prior work in designing underwater VLC systems and exploration of underwater optical wireless channels have largely been limited to theoretical propositions. Experimentation work in this space have not studied the impact of water quality factors on communication, and there exists no clear database of underwater VLC channel studies. Prior work have also largely focused only on horizontal configurations, where the transmitter and receiver are at the same depth underwater. To bridge this gap between existing work and advancement of underwater VLC systems, this paper presents the development of an empirical model for underwater VLC across the vertical configuration and across different water quality factors such as turbidity, salinity and temperature.

Experimental demonstration of underwater wireless optical OFDM communication system with a single SPAD receiver

To satisfy the demands of high data rate underwater applications such as image and real-time video transmission, underwater visible light communication (UVLC) has emerged as an alternative to acoustic signaling. One of the major impairments in UVLC systems is turbulence-induced fading as a result of temporal variations in temperature and salinity. Furthermore, unlike the horizontal links modeled with fixed turbulence strength, vertical links experience varying turbulence strength based on the depth-dependent temperature and salinity profiles. In this paper, we consider a multiple-input multiple-output (MIMO) UVLC link over a vertical turbulence channel which is modeled as the concatenation of multiple non-mixing layers. Under the assumption of cascaded log-normal channel model, we derive the outage probability of vertical MIMO UVLC links and quantify the diversity gain in terms of the number of transmitter/receivers.

Non-linearity of power amplifiers (PA) is the restriction of waveform design for 5G Evolution (5GE) and 6G in high frequency bands with large bandwidth. The low peak to average power ratio (PAPR) single carrier waveform discrete fourier transform spreading orthogonal frequency division multiplexing (DFT-s-OFDM) can be used as a candidate waveform for 5GE and 6G. To satisfy the extremely high data rate requirement in 6G, how to keep the low PAPR of single carrier waveform and improve its spectral efficiency (SE) will be the challenge. In our previous study, a novel non-orthogonal waveform (NOW) design was proposed by combining DFT-s-OFDM with FTN to improve SE with low PAPR. However, due to the spectrum extension caused by FTN, large out of band emission (OOBE) and signal-to-noise ratio (SNR) loss were observed. In this paper, we propose an enhanced non-orthogonal waveform (eNOW) to improve the SE of DFT-s-OFDM by designing the frequency domain power allocation to efficiently suppress OOBE and reduce SNR loss. Simulation results demonstrate that the OOBE and SNR loss of eNOW can be significantly reduced than that of NOW by design the frequency domain power allocation. With eNOW scheme, OOBE, PAPR and throughput gain can be achieved, simultaneously.

Discrete Fourier Transform Spread Orthogonal Frequency Division Multiplexing(DFT-S-OFDM) is widely applied to the next generation wireless communication, because of its good performance in band utilization, high ability to resist multipath interference, low peak to average power ratio and low emitted energy. Traditional data security methods can not effectively protect the air interface and implement complex. This paper proposed a new physical layer encryption algorithm. The algorithm utilized the initial value of chaotic sequence as seed key, under its control produced two complex diagonal key matrices, multiplied by the two matrices respectively before and after the N-point DFT transform, through this method we implement the data encryption. The algorithm is easy to implement, does not change the PAPR of original system and bit error rate of the system, can resist brute-force attack and plaintext cipher-text attack.

In highly time-varying fading channel, the Discrete Fourier Transform Spreading Orthogonal Frequency Division Multiplexing (DFTS-OFDM) signal would be damaged significantly by the inter-channel interference (ICI) due to the loss of orthogonality among subcarriers which leads a fatal degradation of bit error rate (BER) performance. To solve this problem, this paper proposes a time domain equalization (TDE) technique in conjunction with a time domain channel impulse response (CIR) estimation method for the DFTS-OFDM signal without using a guard interval (GI). The features of proposed method is to employ a time domain training sequence (TS) both for the estimation of time domain CIR at every sampling time and for removing the inter-symbol interference (ISI) incurred in the multipath fading channel. The proposed method also employs the TDE with a maximum likelihood (ML) estimation method in the demodulation of received time domain signal at every symbol instead of using the conventional Minimum Mean Square Error-Frequency Domain Equalization (MMSE-FDE) method. This paper presents various simulation results to demonstrate the effectiveness of proposed demodulation method for the DFTSOFDM signal without GI as comparing with the conventional MMSE-FDE and TDE methods both of usingGI under highly mobile environments.