Sunish Kumar Orappanpara Soman

This paper presents an innovative rapid-convergence learning framework based on the extreme learning machine (ELM) for the estimation of the linear and nonlinear signal-to-noise ratio (SNR) components in coherent optical fiber communication systems. The ELM estimator jointly obtains both SNR components using input features based on the mean squared error (MSE) between the transmitted and the received pilot symbols, as well as the entropy extracted from the amplitude values of the received data symbols. The MSE provides a statistical measure of the errors between the transmitted and received pilot symbols, whereas the entropy quantifies the dispersion within the received data symbols. These input features provide valuable insights into the characteristics of both linear and nonlinear SNR components. A comprehensive computational complexity analysis of the proposed ELM estimator is carried out in terms of real multiplications and additions, to quantify its computational demands. Numerical results reveal that the proposed ELM estimator offers substantial advantages in training time, estimation accuracy, and computational complexity compared to existing estimators in the literature.

This article investigates the secrecy performance of a non-linear energy-harvesting backscatter communication (BackCom) network in the presence of direct link and reconfigurable intelligent surface (RIS) interference. The network comprises a source, multiple passive tags, an RIS, and a legitimate reader, with an eavesdropper attempting to intercept the communication. We analyze a tag selection scheme based on long-short-term memory (LSTM) to address the challenge of selecting tags under the influence of direct link and the RIS interference. The nonideal behavior of the RIS is exploited to enhance secrecy performance by modeling RIS phase errors using Von Mises and uniform distributions. Because of interference from the direct link and the RIS being common to all tags, the secrecy rates of different tags are correlated. The LSTM-based scheme effectively captures this correlation and perfectly matches the conventional selection scheme on low and high tag counts. The secrecy outage probability (SOP) achieved using the LSTM outperforms other machine learning techniques, such as k-nearest neighbors (k-NN), decision trees (DT), and support vector machines (SVM). We also demonstrate the impact of RIS elements, phase error parameters, and the number of tags on the SOP in the considered RIS-aided BackCom network.

This paper presents two innovative, pilot-assisted, neural network (NN)-based signal-to-noise ratio (SNR) estimators for application in optical fiber communications. These estimators, termed pilot-assisted feature complexity reduction (PAF-CR) and pilot-assisted feature accuracy enhancement (PAF-AE), are designed to jointly estimate both linear and non-linear SNR components. The architectures of these proposed estimators employ feedforward NNs (FFNNs) for the SNR estimation, with PAF-CR utilizing a two-hidden layer FFNN and PAF-AE employing a single-hidden layer FFNN. Novel features are extracted from pilot signals to utilize the pilot overhead in transmitted signals, such as mean absolute error and mean signed deviation, which statistically measure the error between transmitted and received pilot signals. Additionally, features are directly extracted from the received data signal, such as average absolute deviation, entropy, and arithmetic mean, to capture its statistical dispersion characteristics. The proposed features are carefully selected to effectively capture the characteristics of both linear and non-linear SNR components. The estimation accuracy of the SNR components achieved by the proposed estimators is evaluated using the normalized root mean square error and the standard deviation of the estimation errors. A comprehensive computational complexity analysis of the proposed PAF-CR and PAF-AE estimators is conducted, expressed in terms of real-valued multiplications and additions. Numerical results illustrate that the proposed PAF-CR and PAF-AE estimators achieve a favorable trade-off between the SNR estimation accuracy and computational complexity compared with existing literature estimators. The proposed PAF-CR offers significant computational complexity reduction with a slight enhancement in estimation accuracy, while the proposed PAF-AE provides substantial estimation accuracy improvement while slightly decreasing computational complexity.

Addressing the vulnerability of multiple eavesdroppers, the challenge of energy harvesting by tags, and the secure transmission requirements in backscatter communication (BackCom) networks, this article investigates the secrecy performance of a passive BackCom network comprising a source, a destination, and multiple tags in the presence of multiple eavesdroppers. We propose four tag selection schemes namely optimal tag selection (OTS), suboptimal tag selection (STS), minimal eavesdropping tag selection (MTS), and random tag selection (RTS) to exploit diversity in delivering information securely from the source to the destination. The OTS scheme relies on complete channel state information (CSI), whereas STS and MTS utilize partial CSI, reducing their complexity compared to the OTS. The RTS scheme employs arbitrary random tag selection without relying on any CSI, serving as a baseline for performance comparison. The secrecy outage probability (SOP) and the ergodic secrecy rate (ESR) are employed as metrics to evaluate the secrecy performance of the system. We present generalized and unified approaches to derive closed-form expressions for SOP and ESR. Additionally, asymptotic closed-form expressions for SOP and ESR are derived for all tag selection schemes, providing valuable insights into the system’s performance with respect to different parameters. Numerical simulation results reveal that, in general, the OTS scheme achieves superior secrecy performance compared to the other selection schemes. However, for a relatively small number of tags, the STS scheme performs comparably to the OTS, making it a cost-effective alternative. It is noteworthy that, for the same level of complexity, the MTS exhibits poor performance when compared to STS and OTS. This makes STS a suitable choice over OTS and MTS, offering an effective balance between the complexity and performance. We further demonstrate the effect of the number of tags, the number of eavesdroppers, and path loss parameters on SOP and ESR performances for the BackCom network under consideration.

This paper proposes a novel two-stage neural network (TSNN)-based signal-to-noise ratio (SNR) estimator tailored specifically for coherent optical fiber systems. The proposed TSNN architecture consists of two distinct NN stages. In the first NN stage, a novel technique called feature estimation using NN (FE-NN) is proposed, aiming to decrease the computational complexity by learning feature similarities and estimating some features based on others generated mathematically from the received signal. Subsequent to the FE-NN stage, a second NN stage is meticulously crafted to jointly estimate the linear and nonlinear SNR components with precision. This stage utilizes a novel set of input features generated exclusively from the received signal, without prior knowledge of the transmitted signals. The proposed input features leverage statistical measures such as median absolute deviation, arithmetic mean, and entropy to provide a comprehensive insight into SNR dynamics, thereby enhancing estimation accuracy. A comprehensive analysis of the computational complexity of the proposed TSNN SNR estimator is provided, quantifying the required number of real-valued multiplications and realvalued additions. Performance evaluation of the proposed TSNN estimator is conducted through extensive simulations encompassing 4950 realizations of a standard single-mode fiber wavelength division multiplexing system, employing dualpolarization 16-ary quadrature amplitude modulation. The results underscore the pronounced reduction in computational complexity achieved by the proposed TSNN estimator compared to the most efficient estimators in the literature. Moreover, the proposed TSNN estimator yields superior accuracy in both linear and nonlinear SNR components estimation, thereby highlighting its efficacy in optical communication systems.

Climate change-induced natural disasters are seriously affecting the planet's ecosystem and disrupting the socioeconomic development of humanity. This exigent issue leads to the developing of novel technologies and methodologies to utilize sustainable energy sources such as geothermal energy for energy generation. However, geothermal reservoirs are highly locationspecific and identifying the potential zones is a daunting task for reservoir engineers. Moreover, the geothermal energy density of a place can change over time due to various environmental factors; therefore, it is crucial to monitor and assess its changes periodically. Concurrently, the accelerating advancements in the research of sixth-generation (6G) wireless networks and their possible disruptive technologies, such as integrated sensing and communications (ISAC), have recently received much attention from the broader research communities. In this article, we envision and introduce the general concept of a generalized geothermal sensing and monitoring (GeoSM) model and explore the role of the ISAC system in the localization and periodic monitoring of geothermal energy. Specifically, we illustrate the envisioned system architecture for the GeoSM model, present a general framework of the 6G-enabled ISAC system, and suggest possible enabling technologies to facilitate the distributed sensing and monitoring of the geothermal energy zones. Then, we describe the geothermal heat network as a prospective use case of the GeoSM model. Following that, we present a preliminary case study on the waveform optimization of our proposed ISAC framework of the envisioned GeoSM model for a specific application scenario. Finally, we outline the open research challenges and discuss possible future research directions.

Fiber nonlinearity is the bottleneck of optical communication systems and is commonly addressed by applying various nonlinearity mitigation and compensation techniques. In general, nonlinearity mitigation techniques offer modest improvements with minimal computational complexity, while nonlinearity compensation techniques provide significant performance gains at the expense of higher computational complexity. This motivates us to propose a joint nonlinearity mitigation and compensation approach in which the nonlinear effects during signal propagation are reduced to compensate for the residual nonlinearity at a lower complexity. Specifically, in this paper, we study the combination of symbol rate optimization (SRO) and perturbation-based nonlinearity compensation (PB-NLC) for a pre-chromatic dispersion compensated (pre-CDC) transmission of polarization multiplexing, digital sub-carrier multiplexing, and wavelength division multiplexing (PM-DSCM-WDM) optical communication system. We highlight the interplay between SRO and PB-NLC and demonstrate that joint SRO and PB-NLC provides considerable performance gain, significant complexity reduction, and an additional degree of freedom to balance performance-complexity trade-offs when compared to applying only PB-NLC in a conventional PM-WDM system. We observe that the pre-CDC transmission manifests a unique property that enables the distribution of PB-NLC computational complexity between transmitter and receiver. Leveraging the distinctive property, we propose a split PB-NLC technique for the PM-DSCM-WDM system. This technique combines the benefits of both pre-PB-NLC and post-PB-NLC, resulting in a modest performance improvement while maintaining the same computational complexity as post-PB-NLC.

Fiber nonlinearity imposes limitations on the transmission distances in optical fiber networks. Fiber nonlinearity compensation (NLC) becomes essential for extending the transmission reach; however, conventional methods like digital backpropagation (DBP) experience challenges related to the intricacies of computational demands. To mitigate the fiber nonlinearity cost-effectively, subcarrier multiplexing (SCM) emerges as a promising solution compared to single-carrier systems. However, the SCM performance is limited by nonlinear effects such as self-subcarrier nonlinearity (SSN) and cross-subcarrier nonlinearity (CSN). In previous studies, a combination of SCM with DBP, named SCM-DBP, has been employed to address these issues. Concurrently, deep learning-assisted NLC, for example, learned DBP (LDBP), has shown promise in enhancing performance and reducing complexity. In this paper, we aim to apply learning to the SCM-DBP by holistically combining the principles of the SCM-DBP and LDBP approaches, denoted as SCM-LDBP, to mitigate SSN and CSN cost-effectively. To investigate the efficacy of our proposed SCM-LDBP technique, we carry out numerical simulations for both a contemporary 32 Gbaud and a strategic 120 Gbaud SCM transmission system over a 1600km optical fiber link. With only two interfering subcarriers in the back-propagation routine, our proposed SCM-LDBP demonstrates a 0.3dB Q factor improvement and a 31.7% complexity reduction in the 32 Gbaud system when compared to the SCM-DBP. Similarly, in the 120 Gbaud system, the proposed SCM-LDBP demonstrates a 0.2dB Q factor improvement and a 37.8% reduction in complexity over the SCM-DBP technique.

This paper proposes two novel neural network (NN) structures to estimate long-term steady linear and nonlinear signal-to-noise ratio (SNR) components in optical fiber communication systems. The first proposed structure is a parallel NN-based (ParNN) estimator, which estimates each SNR component using a different NN structure and input feature set. A combination of gated recurrent unit and dense layers is used to estimate the linear SNR component. On the other hand, the nonlinear SNR component is estimated using a combination of convolutional layer with dense layer. The proposed input features of the ParNN estimator are generated solely from the received signal without knowledge of the transmitted signal. These features are formed of the lower quartile, upper quartile, and entropy, which can accurately characterize the behavior of the SNR components by measuring the received signal spread and uncertainty. For further improvement of the ParNN estimator, an additional stage is added to form the proposed enhanced ParNN (EParNN) estimator. This additional stage consists of two feedforward NNs (FFNNs), each with a single dense layer, where the first FFNN is used to estimate the linear SNR component and the second one estimates the nonlinear SNR component. The input of this additional stage is a combination of the input features and output of the ParNN estimator. The computational complexity is derived for the proposed estimators. The training and testing dataset is built from 16-ary quadrature amplitude modulation of a dual-polarization on a wide range of standard single-mode fiber system configurations, e.g., number of wavelength division multiplexing channels, optical launch power, and number of spans. Numerical results demonstrate that the proposed ParNN estimator achieves better SNR estimation accuracy with comparable computational complexity compared to the most efficient work in the literature. The proposed ParNN estimator can independently estimate each SNR component, in which the complexity per SNR component is reduced. Moreover, the proposed EParNN estimator improves SNR estimation accuracy compared to the ParNN estimator at the expense of a slight increase in the computational complexity.

Fiber nonlinearity effects cap achievable rates and ranges in long-haul optical fiber communication links. Conventional nonlinearity compensation methods, such as perturbation theory-based nonlinearity compensation (PB-NLC), attempt to compensate for the nonlinearity by approximating analytical solutions to the signal propagation over optical fibers. However, their practical usability is limited by model mismatch and the immense computational complexity associated with the analytical computation of perturbation triplets and the nonlinearity distortion field. Recently, machine learning techniques have been used to optimise parameters of PB-based approaches, which traditionally have been determined analytically from physical models. It has been claimed in the literature that the learned PB-NLC approaches have improved performance and/or reduced computational complexity over their non-learned counterparts. In this paper, we first revisit the acclaimed benefits of the learned PB-NLC approaches by carefully carrying out a comprehensive performance-complexity analysis utilizing state-of-the-art complexity reduction methods. Interestingly, our results show that least squares-based PB-NLC with clustering quantization has the best performance-complexity trade-off among the learned PB-NLC approaches. Second, we advance the state-of-the-art of learned PB-NLC by proposing and designing a fully learned structure by adopting the noiseless Manakov equation as the channel propagation model. We apply a bi-directional recurrent neural network for learning perturbation triplets that are alike those obtained from the analytical computation and are used as input features for the neural network to estimate the nonlinearity distortion field. Finally, we demonstrate through numerical simulations that our proposed fully learned approach achieves an improved performance-complexity trade-off compared to the existing learned and non-learned PB-NLC techniques.