In this paper, a wideband photomultiplier tube (PMT)-based underwater wireless optical communication (UWOC) system is proposed and a comprehensive experimental study of the proposed PMT-based UWOC system is conducted, in which the transmission distance, data rate, and attenuation length (AL) is pushed to 100.6 meters, 3 Gbps, and 6.62, respectively. The receiver sensitivity at 100.6-meter underwater transmission is as low as -40 dBm for the 1.5-Gbps on-off keying (OOK) modulation signal. To the best of our knowledge, this is the first Gbps-class UWOC experimental demonstration in >100-meter transmission that has ever been reported. To further minimize the complexity of channel equalization, a sparsity-aware equalizer with orthogonal matching pursuit is adopted to reduce the number of the filter coefficients by more than 50% while keeping slight performance penalty. Furthermore, the performance of the proposed PMT-based UWOC system in different turbidity waters is investigated, which shows the robustness of the proposed scheme. Thanks to the great sensitivity (approaching the quantum limit) and a relatively larger effective area, benefits of misalignment tolerance contributed by the PMT is verified through a proof-of-concept UWOC experiment.

100-m/3-Gbps underwater wireless optical transmission using a wideband photomultiplier tube (PMT).

In this paper, the performance of the conventional Decision Feedback (DFE) equalizer as well as the Volterra DFE is investigated in the context of intensity modulated direct detection optical communications links, when non-return to zero on-off keyed (NRZ-OOK) and optical differential encoded phase shift keyed (NRZ-DPSK) transmission is employed. The DFE is used to mitigate either the residual chromatic dispersion resulting from the incomplete optical compensation or the accumulated chromatic dispersion resulting from uncompensated optical transmission. To facilitate realtime implementation, the equalizer structure is dynamically configured discarding coefficients that have a marginal contribution to the performance of the equalizer leading to a significant computational complexity savings.

Performance evaluation of decision feedback equalizers in fiber communication links

During the last few years, a lot of research has been invested for the development of electronic devices equipped with advanced signal processing techniques for the dispersion compensation of Optical transmission systems Compared to their all-optical counterparts, electronic compensation increases flexibility and gives a new impetus to transparent optical networks for adaptive and dynamic handling in cases where the total accumulated dispersion is not known in advance In this paper, the Sparse Learning via Iterative Minimization (SLIM) algorithm is employed for the design of reduced size Volterra Decision Feedback (VDFE) equalizers in the context of optical communications is considered The equalizer structure is dynamically tuned discarding coefficients that have a marginal contribution to the performance of the equalizer leading to both enhanced convergence speed and significant computational complexity savings

Electronic dispersion compensation of fiber links using sparsity induced volterra equalizers

A hierarchical alternative updated adaptive Volterra filter with pipelined architecture

Super-resolution near-field structure (super-RENS) read-out samples are affected by a nonlinear and noncausal channel, which results in intersymbol interference. Some of these nonlinearities are caused by domain bloom or asymmetry, which is an imperfection in the disc. In this study, we investigate the effect of the domain bloom on the super-RENS read-out signal. For this, we employ the asymmetric symbol conversion scheme to generate asymmetric symbols corresponding to a bit pattern and apply the linear modeling approach to model the read-out channel with a finite impulse response filter. The modeling performance is verified with the causal and noncausal filters in terms of normalized mean square error. The performance of the approach considered here is compatible with that of the Volterra filter, whereas the approach maintains a lower complexity than the Volterra filter.

http://iopscience.iop.org/article/10.7567/JJAP.52.09LE01/pdf

Domain Bloom in Super-Resolution Near-Field Structure Read-Out Signals

In this paper, we investigate the compressed sensing (CS) algorithms for modeling a super-resolution near-field structure (super-RENS) disc system with a sparse Volterra filter. It is well known that the super-RENS disc system has severe nonlinear inter-symbol interference (ISI). A nonlinear system with memory can be well described with the Volterra series. Furthermore, CS can restore sparse or compressed signals from measurements. For these reasons, we employ the CS algorithms to estimate a sparse super-RENS read-out channel. The evaluation results show that the CS algorithms can efficiently construct a sparse Volterra model for the super-RENS read-out channel and that observable nonlinear interactions take place among restricted components in the read-out channel.

A Compressed Sensing Approach for Modeling the Super-Resolution Near-Field Structure Disc System With a Sparse Volterra Filter

We propose an adaptive threshold detector algorithm for multi-level holographic data storage based on the expectation-maximization (EM) method. In this paper, the signal intensities that are passed through the four-level holographic channel are modeled as a four Gaussian mixture with unknown DC offsets and the threshold levels are estimated based on the maximum likelihood criterion. We compare the bit error rate (BER) performance of the proposed algorithm with the non-adaptive threshold detection algorithm for various levels of DC offset and misalignments. Our proposed algorithm shows consistently acceptable performance when the DC offset variance is fixed or the misalignments are lower than 20%. When the DC offset varies with each page, the BER of the proposed method is acceptable when the misalignments are lower than 10% and DC offset variance is 0.001.

Expectation-Maximization Based Adaptive Threshold Detection Algorithm for Multi-Level Holographic Data Storage

In this study, super-RENS disc read-out signals were modeled with various nonlinear models. In the results of modeling the nonlinearity, DC offset, and domain bloom phenomena of the super-RENS channel with a second-order Volterra filter, it was identified that the number of significant quadratic coefficients was relatively small. Therefore, a sparse Volterra model and a truncated Volterra model are applied to effectively model the nonlinear channel. In addition, the Hammerstein-type asymmetric conversion model is employed to model the nonlinear channel for the purpose of comparison. The results demonstrate that the sparse model of a simplified polynomial form achieves good modeling performance for the super-RENS disc read-out signals.

Sparse modeling of super-resolution near-field structure read-out signals

The advantages of the orthogonal frequency division multiplexing (OFDM) are high spectral efficiency, resiliency to RF interference, and lower multi-path distortion. To further utilize vast channel capacity of the multiuser OFDM, one has to find the efficient adaptive subchannel and bit allocation among users. In this paper, we compare the performance of the linear programming dual of the 0-1 integer programming formulation with the existing convex optimization approach for the optimal subchannel and bit allocation problem of the multiuser OFDM. Utilizing tight lower bound provided by the LP dual formulation, we develop a primal heurisitc algorithm based on the LP dual solution. The performance of the primal heuristic is compared with MAO, ESA heuristic solutions, and integer programming solution on MATLAB simulation on a system employing M-ary quadrature amplitude modulation (MQAM) assuming a frequency-selective channel consisting of three independent Rayleigh multi-paths.

A Linear Program Based Heuristic for the Bit and Subchannel Allocation in an OFDM System

For effective dc-free coding in the optical storage systems, guided scrambling (GS) multimode coding is popularly used. To reduce digital discrepancy of the coded sequence, functions of running digital sum (RDS) are used as criteria to choose the best candidate. Among these criteria, the minimum RDS (MRDS), minimum squared weight (MSW), and minimum threshold overrun (MTO) are suggested for effective dc-suppression. In this paper, we formulate integer programming models that are equivalent to MRDS, MSW, and MTO GS coding. Incorporating the MRDS integer programming model in maxmin setting, we develop an integer programming model that computes the worst case MRDS bound given scrambling polynomial and control bit size. In the simulation, we compare the worst case MRDS bound for different scrambling polynomials and control bit sizes. We find that careful selection of scrambling polynomial and control bit size are important factors to guarantee the worst case MRDS performance.

Taehyung Park

Papers

A Compressed Sensing Approach for Modeling the Super-Resolution Near-Field Structure Disc System With a Sparse Volterra Filter

Expectation-Maximization Based Adaptive Threshold Detection Algorithm for Multi-Level Holographic Data Storage

Sparse modeling of super-resolution near-field structure read-out signals

A Linear Program Based Heuristic for the Bit and Subchannel Allocation in an OFDM System

Worst Case Performance Assessment of DC-Free Guided Scrambling Coding by Integer Programming Model