Cognitive Optical Networks Research Articles

Artificial intelligence based quality of transmission predictive model for cognitive optical networks

Due to the advancements in 5 G technologies, high-definition, and the internet of things (IoT), the capacity demand of optical networks has been exponentially increased. Optical communication networks offer several metrics such as high transmission capacity, low transmission loss, better anti-interference, robustness, etc which offers new opportunities to the communication field. To satisfy the increasing demands of optical networks, effective network resource utilization become essential. So, it is needed to design proper planning tools with superior accuracy for quality of transmission (QoT) in optical networks. Recently, artificial intelligence (AI) techniques pose new opportunities for resolving these issues and machine learning (ML) algorithms offer better performance over the analytical approaches. With this motivation, this paper presents a novel AI based cognitive QoT prediction (AI-CQoT) model for optical communication networks. The proposed AI-CQoT model aims to predict the QoT for the quality of service (QoS) link setup using AI techniques with the transmission equation based synthetic data generation. The proposed model uses the Label weighting extreme learning machine (LW-ELM) model for the prediction process which includes a link and signal characteristics as input features. Besides, the LW-ELM model is trained by the use of transmission equations. For improving the predictive performance of the LW-ELM model, the parameters such as weight matrix W and penalty factor C are optimally tuned by the use of the shuffled shepherd optimization (SSO) algorithm. A detailed experimental validation is performed to highlight the improved performance of the AI-CQoT model and the results are investigated in terms of different performance measures.

Modulation format identification using the Calinski-Harabasz index.

Modulation format identification (MFI) is a key technology in optical performance monitoring for the next-generation optical network, such as the intelligent cognitive optical network. An MFI scheme based on the Calinski-Harabasz index for a polarization-division multiplexing (PDM) optical fiber communication system is proposed. The numerical simulations were carried out on a 28 Gbaud PDM communication system. The results show that the required minimum optical signal-to-noise ratio values of each modulation format to achieve 100% identification accuracy are all equal to or lower than their corresponding 7% forward error correction thresholds, and the proposed scheme is robust to residual chromatic dispersion. Meanwhile, the proposed scheme was further verified by 20 Gbaud PDM-QPSK/16QAM/32QAM long-haul fiber transmission experiments. The results show that the scheme has a good reliability when fiber non-linear impairments exist. In addition, the complexity of the scheme is significantly lower than that of other clustering-based MFI schemes.

Cognitive and autonomous QoT-driven optical line controller

In the direction of disaggregated and cognitive optical networks, this work proposes and experimentally tests a vendor-agnostic optical line controller architecture capable of autonomously setting the working point of optical amplifiers to maximize the capacity of a ROADM-to-ROADM (reconfigurable optical add–drop multiplexer) link. From a procedural point of view, once the equipment is installed, the presented software framework performs an automatic characterization of the line, span by span, to abstract the properties of the physical layer. This process requires the exploitation of monitoring devices such as optical channel monitors and optical time domain reflectometers, available, in a future perspective, in each amplification site. On the basis of this information, an optimization algorithm determines the working point of each amplifier to maximize the quality of transmission (QoT) over the entire band. The optical line controller has been experimentally tested in the laboratory using two different control strategies, achieving in both cases a homogeneous QoT for each channel close to the maximum average and an excellent match with respect to emulation results. In this framework, the Gaussian noise simulation in Python (GNPy) open source Python library is used as the physical model for optical propagation through the fiber, and the covariance matrix adaptation evolution strategy is used as an optimization algorithm to identify properties of each fiber span and to maximize the link capacity.

Study on Cognitive Optical Network Structure and Self-optimization with the Application of Artificial Intelligence Technology

Cognitive optical network is the intermediate to combine artificial intelligence technology with network, and also the important network technology to promote network intelligence level constantly. In the paper, it analyzes the cognitive optical network structure with the application of artificial intelligence technology by starting from the basic conditions of cognitive network and cognitive optional network on the basis of fully understanding the connotation of cognitive network and cognitive optical network, and explores its self-governance functions, so as to better realize the self-optimization and self-configuration of network.

Fiber impairments mitigation in OFDM based cognitive optical networks

An adaptive impairments assessment is necessary to evaluate the impact of various linear and nonlinear effects in future generation cognitive optical transport links. In this paper, an Adaptive Fiber Impairment Mitigation (AFIM) algorithm is proposed to identify a suitable mitigation scheme for the cognitive environment. The AFIM algorithm will assess fiber impairments and adaptively select a suitable mitigation scheme with minimum complexity based on the present network conditions and user performance target. The performance of AFIM algorithm is compared with Fixed Fiber Impairment Mitigation approach in terms of outage probability and outage capacity analysis. An Orthogonal Frequency Division Multiplexing based Mode Division Multiplexing system with Few Mode Fiber (FMF) is suggested as a solution to increase the nonlinearity threshold limit of the system. The $$\hbox {L}_2$$ -by-3 nonlinear transform based Peak to Average Power Ratio reduction technique is implemented to mitigate fiber nonlinear effects in FMF based backbone and backhaul links. The performance analysis of the FMF system has been evaluated and compared with that of Single Mode Fiber system. The proposed analytical model and mitigation schemes are integrated with the AFIM algorithm to realize the cognitive optical network. Further, the result shows that AFIM algorithm enhances the system capacity by more than 6-folds at an outage probability of 0.5 and reduces the outage probability to 0.6 at the capacity range of 20 Gbps.

Mitigation of Non Linear Effects Using Non Linear Transform in Dispersion Managed Coherent Optical OFDM Systems

It is very important to compensate the dispersion and non linearity present in the optical backhaul so that it is compatible with upcoming technologies. Optical Frequency Division Multiplexing (OFDM) is a physical layer, Multicarrier Modulation Technique (MCT) which is being widely used to design Back Bone (BB), Back Haul (BH) and Cognitive Optical Networks (CONs). Fiber nonlinearity present in dispersion managed links limits the capacity of an OFDM link, as it gets enhanced by high Peak to Average Power Ratio (PAPR) value of the transmitted signal. This work aims to decrease the PAPR value and hence in turn reduce the non linear impairments present in the coherent OFDM optical fiber link. In this work L2-by-3 non linear transform has been implemented to mitigate various fiber non linear effects through PAPR reduction.

Impairment assessment of orthogonal frequency division multiplexing over dispersion-managed links in backbone and backhaul networks

The past decade has seen the phenomenal usage of orthogonal frequency division multiplexing (OFDM) in the wired as well as wireless communication domains, and it is also proposed in the literature as a future proof technique for the implementation of flexible resource allocation in cognitive optical networks. Fiber impairment assessment and adaptive compensation becomes critical in such implementations. A comprehensive analytical model for impairments in OFDM-based fiber links is developed. The proposed model includes the combined impact of laser phase fluctuations, fiber dispersion, self phase modulation, cross phase modulation, four-wave mixing, the nonlinear phase noise due to the interaction of amplified spontaneous emission with fiber nonlinearities, and the photodetector noises. The bit error rate expression for the proposed model is derived based on error vector magnitude estimation. The performance analysis of the proposed model is presented and compared for dispersion compensated and uncompensated backbone/backhaul links. The results suggest that OFDM would perform better for uncompensated links than the compensated links due to the negligible FWM effects and there is a need for flexible compensation. The proposed model can be employed in cognitive optical networks for accurate assessment of fiber-related impairments.

Cognitive Optical Network Testbed: EU Project CHRON [Invited

The aim of cognition in optical networks is to introduce intelligence into the control plane that allows for autonomous end-to-end performance optimization and minimization of required human intervention, particularly targeted at heterogeneous network scenarios. A cognitive network observes, learns, and makes informed decisions based on its current status and knowledge about past decisions and their results. To test the operation of cognitive algorithms in real time, we created the first operational testbed of a cognitive optical network based on the Cognitive Heterogeneous Reconfigurable Optical Network (CHRON) architecture. In this experiment, an intelligent control plane, enabled by a cognitive decision system (CDS), was successfully combined with a flexible data plane. The testbed was used to test and validate different scenarios, demonstrating benefits obtained by network cognition, particularly lightpath establishment and a teardown scenario, improved failure restoration time, cognitive virtual topology reconfiguration, and a modulation format change.

Blind Optical Modulation Format Identification From Physical Layer Characteristics

We describe and experimentally demonstrate automated identification of optical modulation formats exclusively from physical-layer signal characteristics. The system identifies the data encoding as on-off keyed versus binary or quadrature phase-shift keyed, as well as auxiliary modulation profiles. The symbol clock rate and optimal dispersion compensation are also determined. We have validated this system with over 1500 test cases. These are enabling functionalities for a universal optical receiver and for a cognitive optical network architecture that optimizes utilization of available optical bandwidth. We demonstrate the capabilities of the system using a wavelength division multiplexed signal consisting of 19 channels, with multiple modulation formats and widely varying dispersions, signal strengths, and optical signal-to-noise ratios.

Cognitive optical networks: key drivers, enabling techniques, and adaptive bandwidth services

To support efficient delivery of packet and circuit services simultaneously in future optical networks, software will play more and more important roles, not only in control and management plane, but also in transport plane. This article discusses a new concept of cognitive optical networks utilizing intelligent software (e.g., digital signal processing in the optical transport plane and GMPLS in the optical control plane) as well as flexible optics (e.g., colorless, directionless, contentionless, gridless ROADM, and optical OFDM). The software-defined CON architecture with advanced capabilities such as quality of transmission awareness and client service awareness is capable of provisioning adaptive bandwidth services at wavelength and sub-wavelength levels, making future optical networks more cognitive and reconfigurable.