A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Development of efficient algorithms for path selection and wavelength selection is crucial in minimizing the burst loss probability (BLP) in OBS networks. In this paper, we present novel Reinforcement Learning algorithms for path selection and wavelength selection in the context of OBS networks. We develop an online path selection algorithm based on Q-learning to minimize the BLP by choosing an optimal path among a set of predetermined routes between every pair of ingress and egress nodes. We also propose a Q-learning algorithm for wavelength selection that selects an optimal wavelength among the available wavelengths in a pre-routed path with an objective of minimizing the BLP. We assume no wavelength conversion and buffering to be available at the core nodes of the OBS network. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the best known adaptive techniques for path selection and wavelength selection available in the literature. <s> BIB001 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Contention among the bursts that arrive simultaneously at a core node leads to burst loss which affects the throughput of higher layer traffic. Development of efficient algorithms for path selection and wavelength selection is crucial to minimize the burst loss probability (BLP) in OBS networks. In this paper, we formulate path selection and wavelength selection in OBS networks as a multi-armed bandit problem and discuss the difficulties to solve them optimally. We propose algorithms based on Q-learning to solve these problems near-optimally. At an egress node, the path selection algorithm evaluates the Q values for a set of precomputed paths and chooses a path that corresponds to minimum BLP. Similarly, Q-learning algorithm for wavelength selection selects a wavelength in a pre-routed path such that the BLP is minimized. We do not assume wavelength conversion and buffering at the core nodes and hence, selection of path and wavelength is done only at the edge nodes. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the other adaptive algorithms available in the literature. <s> BIB002 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> In this paper, we propose a Q-learning based deflection routing algorithm that may be employed to resolve contention in optical burst-switched networks. The main goal of deflection routing is to successfully deflect a burst based only on a limited knowledge that network nodes possess about their environment. Q-learning, one of the reinforcement learning algorithms, has been proposed in the past to help generate deflection decisions. The complexity of existing reinforcement learning-based deflection routing algorithms depends on the number of nodes in the network. The proposed algorithm scales well for larger networks because its complexity depends on the node degree rather than the network size. The algorithm is implemented using the ns-3 network simulator. Simulation results show that it has comparable performance to an existing reinforcement learning deflection routing scheme while having lower memory requirements. <s> BIB003 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> We propose a K-means clustering-based approach to plan for the deployment of greenfield passive optical networks (PON) aiming to minimize the total deployment cost. Studies show that the proposed approach is effective to significantly reduce the deployment cost compared to a benchmark random-cut approach. <s> BIB004 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> We examine EDFA power excursions during three defragmentation methods of flexgrid super-channels. Using a machine learning approach, we demonstrate automatic and dynamic adjustments of pre-EDFA power levels, and show the mitigation of post-EDFA power discrepancy among channels by over 62%. <s> BIB005 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> We propose a novel artificial intelligence (AI) assisted framework to realize virtual network (VNT) slicing in an inter-datacenter optical network (IDCON), where the infrastructure provider (InP) performs resource advertising and pricing based on deep reinforcement learning (DRL) and grants the virtual network embedding (VNE) schemes calculated distributedly by the tenants. Simulation results confirm that compared with the traditional centralized VNT slicing framework, our proposal can not only make the InP more profitable but also relieve its computation complexity effectively. <s> BIB006 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> We first propose a novel multi-domain routing paradigm that transforms the routing problem from heuristic-algorithm-based computation to artificial-intelligence-based data analytics. Numerical results prove that our proposal can achieve excellent routing accuracy, and significant signaling reduction. <s> BIB007 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> Abstract With more and more human underwater activities carrying out, underwater visible light communication has received increasing attention worldwide. To solve the problem of noise and nonlinearity in underwater visible light communication systems, we proposed a phase deviation correction algorithm based on K-means clustering to correct the phase deviation of special-shaped 8-QAM constellations. The experimental results indicate the performance of the circle has the maximum improvement after using the phase deviation correction algorithm. Before the use of the K-means phase correction algorithm, the highest data rate that circle can achieve is 1.3875 Gbps. The highest data rate increases to 1.4625 Gbps after applying the algorithm. <s> BIB008 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> Amplitude jitter is a common phenomenon in pulse amplitude modulation (PAM) visible light communication (VLC) systems, which greatly affects the system performance. In this paper, we propose a novel time-amplitude two-dimensional re-estimation based on density-based spatial clustering of applications with noise (DBSCAN) algorithm of machine learning (2DDB) to distinguish different signal levels with jitter. The Q factor of a PAM-8 VLC system applying 2DDB is improved by 1.6–3.2 dB through experimental demonstration. In addition, we also investigate the influence of amplitude jitter with different levels on PAM-8 systems, and analyze the range of parameters and applications of a DBSCAN algorithm. To the best of our knowledge, this is the first time that DBSCAN of machine learning is successfully applied to PAM VLC systems. <s> BIB009 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> The rise of Industry 4.0 is pushing more machine devices into the connection of the communication network. Internet of Things (IoT) emerges as an enabling technique to support automatic information transmission and the universal interconnection among massive machine devices. Unlike the human type communication (HTC) in the conventional Internet, IoT features machine type communication (HTC) with small data granularity and massive connections, which impose new challenges on the network resource allocation earlier designed for HTC communication. One of the challenging issues is how we can manage the network resource for the IoT service provisioning. As a promising network architecture for the “last-mile” access in 5G communication, Fiber-Wireless (FiWi) access network compromises the high bandwidth capacity of the optical access network and the flexibility of wireless access network. Especially, network virtualization as an emerging technique provides FiWi with the feasibility for the coordinated bandwidth allocation of wireless access network and optical access network. The evolution of FiWi access network should be not only oriented to the technological breakthrough in regular traffic, but also be supportive of the emerging IoT traffic. In this paper, we focus on the resource allocation problem in FiWi access network supporting IoT service. We first analyze the dynamics of regular traffic and propose the traffic prediction method based on Q-Learning. Then, we propose a virtual network embedding algorithm to map exactly the virtual networks of IoT traffic to the idle resource of the virtual networks of regular traffic without degrading the performance of regular traffic. Thus, the proposed algorithm can improve the resource utilization more effectively. The simulation results demonstrate that the proposed algorithm can achieve a higher acceptance ratio and lower migration ratio of IoT traffic. <s> BIB010 </s> A Survey on Machine Learning for Optical Communication [Machine Learning View] <s> Network: <s> As the services in elastic optical networks (EONs) are bandwidth-intensive, unpredictable and dynamic, increasing network factors are emerging to affect the performance of network survivable planning and operation from network capacity to efficiency. Most of the traditional protection and restoration approaches may become before long inefficient due to the improvement of a particular network performance metric always at the expense of others. We argue that it would be more beneficial for comprehensive optimization of network performance to consider main network metrics jointly. Moreover, the highly dynamic features of EONs call for the new generation of machine learning-based solutions that are flexible and adaptable to cope with the dynamic nature of services to perform analytics. In this paper, we investigate the problem of global optimization of network performance under survivable EON environment. Specifically, a criterion, named the whole network cost-effectiveness value with survivability (WCES), is defined to measure the overall network performance by balancing the interaction among main network metrics. Then we propose a deep reinforcement learning (DRL) -based heuristic with the objective of improving overall network performance, in which two agents are utilized to provide working and protection schemes converging toward better survivable routing, modulation level and spectrum assignment (S-RMLSA) policies. Numerical results show that the proposed criterion can efficiently measure the overall network performance, and the double-agent DRL-based heuristic can greatly improve WCES while ensuring the network survivability and paying the acceptable extra consumption of request blocking probability. <s> BIB011
The fifth application of DNN is in optical networks, which is only limited to one investigation. Bidirectional LSTM is designed for combating drawbacks of MLP, and time delay NN (TDNN) (Fig.13.c) , on the input data flexibility (MLP, and TDNN have fixed input data), and RNN, on the future input data (RNN cannot achieve future data). Bidirectional LSTM-RNN increase the amount of input data by connecting two opposite directional hidden layers (backward and forward states) to the same output ( Fig.13.b) . BIB007 proposed a multi-domain routing paradigm that transforms the inter-domain routing problem from heuristic-algorithm-based computation to AI-based data analytics. Maintaining channel power stability during fast-changing spectrum utilization is crucial to ensuring the QoS in flex grid OC networks. These networks require spectrum defragmentation to improve spectral efficiency. Various defragmentation methods, e.g., hop, make before break, sweep, and interact with the power dynamics of EDFA differently. EDFA power excursions is a challenge for network operations and can result in exacerbated post-EDFA power discrepancy during spectral defragmentation processes. ML could be used to accurately predict the power excursions in WDM channel provisioning, leads to quick and precise decisions addressing network demands, and optimizes EDFA power dynamics. Lasso regression (Fig.16.b) adds an L1 regularization term to the cost function, and result in sparse models with few coefficients. If someone wants not to eliminate weights, but reduce non-important ones, use of larger regularization term is a good choice. Ridge regression is a linear regression with additional L2 regularization term. In Kernelized Bayesian regression (Fig.16.c) , first input features are mapped to another higher dimension, then regression model determines the posterior pdf of it. These techniques in a fully automated workflow extract EDFA power dynamics and adjust power without iterative power measurements [109] BIB005 . The only instigation of k-means in OC networks is the deployment of green field passive networks to minimize the total deployment cost BIB004 . WOC: by increase in human underwater activities, underwater VLC attracted many attentions toward itself. K-means algorithm, despite less complexity, could deal with noise and nonlinearity, as well as amplitude and phase jitter (as the main drawbacks of VLC system). For example, BIB008 proposed a k-means clustering technique to correct the phase deviation of special-shaped 8-QAM constellations. As another example, BIB009 proposed a time-amplitude two-dimensional re-estimation based on DBSCAN algorithm to distinguish different signal levels with jitter. DBSCAN groups together a set of points of a space (high density region), and marks as outlier the points that are in low-density regions. The Q-learning is used in deflection routing to resolve problems of OBS networks BIB002 BIB003 BIB001 . In OBS networks, efficient algorithms are very important for choosing path / wavelength that minimizes the burst loss probability; however, it is proved that Q-learning could deal with this issue BIB001 . At an output node, the path/wavelength selection algorithm calculates the Q values for a set of pre-computed path/wavelength and choses one with the minimum burst loss probability BIB002 . It is shown that Q-learning could effectively deal with resource allocation problem of Fi-Wi access network supporting IoT service BIB010 . For this aim, first the dynamics of regular traffic should be analyzed and then Qlearning be used for traffic prediction. Definition: Deep reinforcement learning (DRL) (Fig.19.c) combines deep learning and reinforcement learning and generates algorithms that can be used in complicated applications that require flexibility e.g., in complicated optical networks. Deploying DL (e.g. DNN) in RL (e.g. Q-learning) generates a powerful tool (DRL) that is capable to scale to unsolvable problems. The flexibility of DRL could also be helpful in EONs, which are highly dynamic. BIB011 defined a criterion, named the whole network cost-effectiveness value with survivability, and proposed a performance optimization under survivable EON environment. Then a DRL improves performance, in which two agents are proposed to provide working and protection schemes converging toward better survivable routing, modulation level, and spectrum assignment policies. BIB006 presented a DRL framework for realizing virtual network slicing in inter-datacenter network, in which based on DRL, the infrastructure provider advertises resource and pricing and grants the virtual network embedding schemes.