Recent progress in and perspectives of underwater wireless optical communication

In this paper, we propose a novel nonlinear resilient learning post equalizer named TFDNet in UVLC system. Unlike the traditional deep neural network (DNN) based post equalizers which merely consider the time domain, the proposed TFDNet exploits time-frequency image analysis which considers the time and frequency domains simultaneously and transforms the signal into 2D time-frequency image, which is further learned by neural network. Experimental results demonstrate that TFDNet outperforms Volterra and DNN based methods for compensating nonlinear distortions through a 1.2 m underwater channel using 64 quadrature amplitude modulation-carrierless amplitude modulation (64QAM-CAP). Even under severe nonlinear distortions where Volterra and DNN cannot work, TFDNet retains valid bit error rate (BER) below the 7% forward error correction (FEC) limit of 3.8 × 10−3. The performance of TFDNet verifies the effectiveness of time-frequency image analysis which has been applied to tackle nonlinear distortions in UVLC system for the first time.

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Nonlinear Resilient Learning Method Based on Joint Time-Frequency Image Analysis in Underwater Visible Light Communication

Theory of optical turbulence in underwater medium and the effects of underwater turbulence on various applications done in underwater or under ocean are reviewed. A detailed survey of underwater turbulence studies in literature is reported. Underwater physics covering salinity, temperature and dissipation rates, various power spectra such as Hill, Nikishov and Nikishov, Li, new form and the oceanic turbulence optical power spectrum (OTOPS) spectra are explained. Wave and phase structure functions, related coherence length, anisotropy, intensity, field correlations in underwater turbulence are elaborated. Scintillation indices of spherical, plane, Gaussian, and other types of optical beams are mentioned. Bit-error-rate (BER), signal-to-noise-ratio (SNR) performances of optical wireless communication systems operating in underwater, and the effects of modulation types of these systems on the performances are reported. Channel capacity of underwater optical wireless communication systems when the channel experiences log-normal, gamma-gamma, Weibull, and negative exponential statistics are reflected. Underwater imaging and the related modulation transfer function, underwater turbulence mitigation techniques in the form of aperture averaging, adaptive optics, receiver, transmitter and multiple-input, multiple-output (MIMO) spatial diversity techniques are revised. 

Underwater turbulence, its effects on optical wireless communication and imaging: A review

Deep neural network has been used to compensate the nonlinear distortion in the field of underwater visible light communication (UVLC) system. Considering the tradeoff between the equalization performance and the network complexity is the priority in practical applications. In this paper, we propose a novel hybrid frequency domain aided temporal convolutional neural network (TFCNN) with attention scheme as the post-equalizer in CAP modulated UVLC system. Experiments illustrate that the proposed TFCNN can achieve better equalization performance and remain the bit error rate (BER) below the 7% hard-decision forward error correction (HD-FEC) limit of 3.8×10-3 when other equalizers loss effectiveness under serious distortion condition. Compared with the standard deep neural network, TFCNN shows 76.4% network parameters complexity reduction.

Hybrid frequency domain aided temporal convolutional neural network with low network complexity utilized in UVLC system.

In this paper, a simulation was proposed to study underwater visible light communication (UVLC) system using a multiple input–single output (MISO) technique with different modulation schemes under coastal water. MISO technique has enhanced the distance link with high speed data rate compared with the traditional technique of single input–single output (SISO), MISO technique was also employed to examine the performance of BER and data rate under different Jerlov types of coastal water. Various impairments such as distance link, pathloss, channel loss and the attenuation of coastal water were introduced into the transmission of the optical signal to assess the reliability of the MISO technique. The simulation analysis shows the proposed technique can outperform the SISO technique in terms of BER and Q. factor. Moreover, the Q. factor of up to 16 dB and 10−10 BER under J9C of coastal water by using (4Tx/1Rx) technique. The radiation angle of LED and the attenuation coefficient of coastal water were restricted for achieving longer distances link. The system accomplished a distance link about 2.9 and 13.5 m for coastal water types J9C and JIC respectively. The 32-PPM is a suitable modulation for obtaining a best distance link for LoS and NLoS scenarios.

Investigation of multiple input–single output technique for wireless optical communication system under coastal water

In this paper, recent advances in underwater wireless optical communication (UWOC) are reviewed for both LED- and LD-based systems, mainly from a perspective of advanced modulation formats. Volterra series-based nonlinear equalizers, which can effectively counteract the nonlinear impairments induced by the UWOC system components, are discussed and experimentally demonstrated. Both the effectiveness and robustness of the proposed Volterra nonlinear equalizer in UWOC systems under different water turbidities are validated. To further approach the Shannon capacity limit of the UWOC system, the probabilistic constellation shaping technique is introduced, which can overcome the inherent gap between a conventional regular quadrature amplitude modulation (QAM) format and the Shannon capacity of the channel. The experimental results have shown a significant system capacity improvement compared to the cases using a regular QAM.

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Yang Guo

Papers

High-speed underwater wireless optical communications: from a perspective of advanced modulation formats [Invited]