Ramachandran Venkatesan

The first-order (FO) perturbation theory-based nonlinearity compensation (PB-NLC) technique has been widely investigated to combat the detrimental effects of the intra-channel Kerr nonlinearity in polarization-multiplexed (Pol-Mux) optical fiber communication systems. However, the NLC performance of the FO-PB-NLC technique is significantly limited in highly nonlinear regimes of the Pol-Mux long-haul optical transmission systems. In this paper, we extend the FO theory to second-order (SO) to improve the NLC performance. This technique is referred to as the SO-PB-NLC. A detailed theoretical analysis is performed to derive the SO perturbative field for a Pol-Mux optical transmission system. Following that, we investigate a few simplifying assumptions to reduce the implementation complexity of the SO-PB-NLC technique. The numerical simulations for a single-channel system show that the SO-PB-NLC technique provides an improved bit-error-rate performance and increases the transmission reach, in comparison with the FO-PB-NLC technique. The complexity analysis demonstrates that the proposed SO-PB-NLC technique has a reduced computational complexity when compared to the digital back-propagation with one step per span.

Underwater acoustic sensor networks (UWSNs), acting as a reliable and efficient infrastructure of the Internet of underwater things (IoUT), have attracted much research interest in recent years due to the wide range of their potential marine applications. The limited energy supply of underwater sensor nodes is a significant challenge that can be mitigated by the cyclic difference set (CDS)-based coordination asynchronous wake-up scheme. However, the CDS-based asynchronous wake-up scheme also introduces long delays in the neighbor discovery that deteriorates packet delay as well as the network lifetime. In this paper, we formulate the problem of policy selection for idle listening as a Markov decision process and exploit the framework of deep reinforcement learning to obtain the optimal policies of underwater sensor nodes. Furthermore, the long short-term memory (LSTM) networks are utilized to estimate the network traffic feature, which can improve the performance of the proposed adaptive asynchronous wake-up scheme. To verify the performance of the proposed scheme, simulations in different network scenarios are conducted with the comparison of random, fixed metric policies, and original CDS-based asynchronous wake-up schemes.

The regular perturbation (RP) series used to analytically approximate the solution of the nonlinear Schrodinger equation has a serious energy divergence problem when truncated to the first-order. The enhanced RP (ERP) method can improve the accuracy of the first-order RP approximation by solving the energy divergence problem. In this paper, we propose an ERP-based nonlinearity compensation technique, referred to as ERP-NLC, to compensate for the fiber nonlinearity in a polarization-division multiplexed dispersion unmanaged optical communication system. We also propose a modified perturbation-based NLC (PB-NLC) technique by simple phase-rotation (PR) of the nonlinear coefficient matrix, referred to as the PR-PB-NLC. The PR-PB-NLC can be considered as a by-product of the ERP-NLC technique. We show through numerical simulation that, for a 256 Gb/s single-channel system, the proposed ERP-NLC technique improves the Q-factor performance by ~1.2 dB and ~0.6 dB when compared to the electronic dispersion compensation (EDC) and the PBNLC techniques, respectively, at a transmission distance of 2800 km. Also, the result for a 1.28 Tb/s wavelength-division multiplexed five-channel transmission system at the same transmission distance shows that the Q-factor performance of the ERP-NLC technique is improved by ~0.6 dB and ~0.4 dB when compared to the EDC and the PB-NLC techniques, respectively. The simulation results for the PR-PB-NLC technique for a single or five-channel transmission system shows an improved Q-factor performance when compared to the EDC and PB-NLC techniques. Finally, we show that the proposed performance enhancement comes with a negligible increase in the computational complexity for the ERP-NLC and PR-PB-NLC techniques when compared to the PB-NLC technique.

This paper investigates the estimation and prediction of the sparse time-varying channel in underwater acoustic communication systems. The estimation approach exploits the sparse structure of the delay-Doppler representation of the channel. Various state-of-the-art matching pursuit (MP)-type algorithms for sparse signal reconstruction are compared. Among the considered algorithms, sparsity adaptive MP (SaMP) and its variant adaptive step size SaMP have the advantage of not requiring a priori knowledge of the sparsity level and outperform the other algorithms in terms of mean squared error (MSE). Moreover, due to the fast time-varying nature and the extremely limited bandwidth of the UWA channels, a channel prediction that can provide up-to-date channel state information is necessary for reliable symbol detection. This paper proposes an adaptive channel prediction scheme that extrapolates the channel knowledge estimated from a block of training symbols, and the predicted channel is used to decode consecutive data blocks. The proposed scheme does not require any a priori knowledge of channel dynamic model and noise statistics, and is able to provide future channel estimates based solely on current channel estimates. Furthermore, the proposed scheme operates in the delay-Doppler domain, and thus has a remarkably lower computational complexity when compared with the channel prediction in time domain. To further improve the prediction accuracy, past detected symbols are fed back to assist the proposed predictor with an up-to-date channel estimate. Simulation results of the proposed channel estimation and prediction demonstrate a good tradeoff between the MSE/bit error rate and the computational complexity.

We investigate the performance of a machine learning classi?cation technique, called the Parzen window, to mitigate the ?ber nonlinearity in the context of dispersion managed and dispersion unmanaged systems. The technique is applied for detection at the receiver side, and deals with the non-Gaussian nonlinear effects by designing improved decision boundaries. We also propose a two-stage mitigation technique using digital back propagation and Parzen window for dispersion unmanaged systems. In this case, digital back propagation compensates for the deterministic nonlinearity and the Parzen window deals with the stochastic nonlinear signal-noise interactions, which are not taken into account by digital back propagation. A performance improvement up to 0.4 dB in terms of Q factor is observed.

The early detection of a kick and mitigation with appropriate well control actions can minimize the risk of a blowout. This paper proposes a downhole monitoring system, and presents a dynamic numerical simulation of a compressible two-phase flow to study the kick dynamics at downhole during drilling operation. This approach enables early kick detection and could lead to the development of potential blowout prevention strategies. A pressure cell that mimics a scaled-down version of a downhole is used to study the dynamics of a compressible two-phase flow. The setup is simulated under boundary conditions that resemble realistic scenarios; special attention is given to the transient period after injecting the influx. The main parameters studied include pressure gradient, raising speed of a gas kick, and volumetric behavior of the gas kick with respect to time. Simulation results exhibit a sudden increase of pressure while the kick enters and volumetric expansion of gas as it flows upward. This improved understanding helps to develop effective well control and blowout prevention strategies. This study confirms the feasibility and usability of an intelligent drill pipe as a tool to monitor well conditions and develop blowout risk management strategies.

By any measure, the various services and applications which are crucial to today's society rely on fast, efficient, and reliable information exchange. Nowadays, most of this information traffic is carried over long distances by optical fiber, which has intrinsic advantages such as wide transmission bandwidth and low attenuation. However, continuing traffic growth has imposed many challenges, especially for equipment manufacturers who have to develop optical transmission solutions to handle the demand for higher data rates without incurring increased capital and operational costs. A feasible approach to overcoming these challenges is to scale the channel capacity by employing orthogonal frequency division multiplexing (OFDM) super-channels. However, OFDM is sensitive to synchronization errors, which can result in complete failure of the receiver-based digital signal processing. Measurement results of various existing OFDM synchronization methods have highlighted inherent limitations with regards to poor system performance, which determines the quality-of-service level perceived by the end user, and complexity, which throws doubts as to their suitability for implementation in actual equipment. In this article, we provide a brief overview of optical transmission systems and some of their performance specifications. We then present a simple, robust, and bandwidth-efficient OFDM synchronization method and carry out measurements to validate the presented synchronization method with the aid of an experimental setup.

In wireless sensor networks, there are usually a small number of position-aware nodes, referred to as anchors. Every other node, called target node (or sensor), can estimate its distances to surrounding anchors according to the received signal strength. Then, trilateration can be employed for self localization. This system is low-cost, simple, and thus popular, but it suffers from two major drawbacks. The first problem is the requirement for realtime channel parameters, which should be estimated and transmitted to target sensors by a central node. Another problem arises from the fact that positioning accuracy is dependent on number of anchors, but anchor density is quite limited for economic consideration in the conventional localization systems. To solve these problem, we proposed in this paper, a new localization framework for wireless sensor networks, which employs a mobile node to serve as virtual anchors. This new framework has two advantages over the conventional one: 1) Offline measurements are totally unnecessary; 2) the number of virtual anchors can be vastly increased with negligible cost, leading to very high positioning accuracy. To justify the superiority of this new framework, extensive analysis and simulations were conducted. Besides, we implemented it on a drone and did field experiments, which showed very promising results.

In the context of long-haul Nyquist wavelength division multiplexed (WDM) superchannel system, a fifth-order Volterra-based nonlinear equalizer (VNLE) is proposed to compensate for the optical fiber nonlinear effects, which represent the major fiber impairment in such high data rate systems. A performance comparison of the fifth-order VNLE with the benchmark digital-back propagation (DBP) and the third-order VNLE is provided. We show that the fifth-order VNLE better combats the fiber nonlinearity in comparison with the third-order VLNE and exhibits closer performance to the DBP. A significant improvement of the performance in terms of the Q factor, nonlinear threshold, and transmission reach is observed when compared with the third-order VNLE.