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A Comprehensive Survey on Machine and Deep Learning for Optical Communications

IEEE ACCESS 2025年 13卷 88794-88846页

作者： Amirabadi, Mohammad Ali Nezamalhosseini, S. Alireza Kahaei, Mohammad Hossein Chen, Lawrence R. Ferdowsi Univ Mashhad Dept Elect Engn Mashhad *** Iran Iran Univ Sci & Technol Sch Elect Engn Tehran *** Iran McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0G4 Canada

The increasing complexity of optical communication systems and networks necessitates advanced methodologies for extracting valuable insights from vast and heterogeneous datasets. Machine learning (ML) and deep learning (DL) have emerged as pivotal tools in this domain, revolutionizing data analysis and enabling automated self-configuration in optical communication systems. Their adoption is fueled by the growing intricacy of systems and links, driven by numerous adjustable and interdependent parameters. This complexity is particularly evident in areas such as coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While ML and DL offer immense potential, their application in optical communications is still in its early stages, with significant opportunities remaining unexplored. Many algorithms have yet to be fully deployed in practical settings, underscoring the emerging nature of this research area. This paper presents a comprehensive survey of ML and DL applications across optical fiber communication (OFC), optical wireless communication (OWC), and optical communication networking (OCN), highlighting the challenges, current advancements, and future potential of these approaches. To address the identified gaps, this survey evaluates and compares ML and DL algorithms in terms of their performance, complexity, objectives, input data, metrics, and applications in optical communication. Specific emphasis is placed on identifying how these algorithms enhance system performance and optimization. Furthermore, the advantages and limitations of existing methods are analyzed, offering a clear perspective on the role of ML and DL in this domain. The survey also includes updated visual representations and domain-specific examples to elucidate the practical applications of ML and DL in OFC, OWC, and OCN. It concludes by discussing the open challenges, proposing potential soluti

关键词： Optical fiber communication Optical fiber networks Optical signal processing Surveys Routing Resource management signal processing algorithms Optical fibers Integrated optics Adaptive optics Machine learning deep learning optical fiber communication optical communication networking optical wireless communication

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Joint Device Activity Detection, Channel Estimation and signal Detection for Massive Grant-Free Access via BiGAMP

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1200-1215页

作者： Zhang, Shanshan Cui, Ying Chen, Wen Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China Hong Kong Univ Sci & Technol Guangzhou IoT Thrust Guangzhou 511400 Peoples R China Hong Kong Univ Sci & Technol Dept Elect Engn Hong Kong Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200240 Peoples R China

Massive access has been challenging for the fifth generation (5 G) and beyond since the abundance of devices causes communication overload to skyrocket. In an uplink massive access scenario, device traffic is sporadic in any given coherence time. Thus, channels across the antennas of each device exhibit correlation, which can be characterized by the row sparse channel matrix structure. In this work, we develop a bilinear generalized approximate message passing (BiGAMP) algorithm based on the row sparse channel matrix structure. This algorithm can jointly detect device activities, estimate channels, and detect signals in massive multiple-input multiple-output (MIMO) systems by alternating updates between channel matrices and signal matrices. The signal observation provides additional information for performance improvement compared to the existing algorithms. We further analyze state evolution (SE) to measure the performance of the proposed algorithm and characterize the convergence condition for SE. Moreover, we perform theoretical analysis on the error probability of device activity detection, the mean square error of channel estimation, and the symbol error rate of signal detection. The numerical results demonstrate the superiority of the proposed algorithm over the state-of-the-art methods in DAD-CE-SD, and the numerical results are relatively close to the theoretical analysis results.

关键词： Channel estimation Sparse matrices signal processing algorithms Approximation algorithms Performance evaluation Coherence time Symbols Bilinear generalized approximate message passing (BiGAMP) device activity detection massive grant-free access signal detection state evolution

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Tensor-Based Sparse Bayesian Learning With Intra-Dimension Correlation

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 31-46页

作者： Song, Yuhui Gong, Zijun Chen, Yuanzhu Li, Cheng Mem Univ Dept Comp Sci St John NL A1B 3X5 Canada HKUST IoT Thrust Guangzhou 999077 Peoples R China HKUST Dept ECE Hongkong Peoples R China Queens Univ Sch Comp Kingston ON K7L 2N8 Canada Mem Univ Dept Elect & Comp Engn St John NL A1B 3X5 Canada

Unlike the flat-view matrix, tensor provides an elegant representation to preserve the structure of data in the multidimensional nature. The wide use of tensor-based approaches highlights the limitation of matrix-based methods and has generated increasing needs in multidimensional data analytics. Driven by such demands, tensor decomposition with automatic rank determination has recently emerged as a promising tool for image processing, wireless communications and neural network compression. However, unlike the success in matrix-based schemes of exploring the correlation for performance improvement, existing tensor-based methods fail to consider the correlation structure in the tensor. In this paper, we propose to exploit the tensor intra-dimension correlation within the framework of sparse Bayesian learning. We provide the sparsity-inducing probabilistic models under both canonical polyadic decomposition and Tucker decomposition to capture the correlation structure effectively. We derive an efficient model inference method with a fast convergence rate based on the expectation-maximization algorithm. Furthermore, a complexity reduction method is proposed to speed up the computation by exploiting several properties of the Kronecker product. We further extend the proposed framework for the incomplete tensor corrupted by sparse outliers to achieve effective tensor completion and outlier mitigation. Simulation results demonstrate the superior performance of the proposed framework over those ignoring the intra-dimension correlation in terms of accuracy and sparsity.

关键词： Tensors Correlation Bayes methods Matrix decomposition Sparse matrices Computational modeling signal processing algorithms Tensor canonical polyadic decomposition Tucker decomposition intra-dimension correlation sparse Bayesian learning expectation-maximization

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Real-Time Polarization Demultiplexing by Multi-Thread Constant Modulus Algorithm for Fully Softwarized Access Networks

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第5期41卷 1346-1356页

作者： Suzuki, Takahiro Kim, Sang-Yuep Kani, Jun-ichi Yoshida, Tomoaki NTT Corp NTT Access Network Serv Syst Labs Yokosuka Kanagawa 2390847 Japan

Optical access networks are replacing dedicated hardware-based architectures with architectures centered on general-purpose servers based on network function virtualization (NFV) and the software-defined network (SDN) in order to efficiently support various new services. To maximize the advantage of this new approach, studies are now targeting the fully softwarized access network which requires softwarization of digital signal processing (DSP). However, polarization demultiplexing includes sequential processing, and it is difficult to softwarize high-speed filter updates in a constant modulus algorithm (CMA) that supports real-world state of polarization (SOP) change. This paper proposes a novel multi-thread CMA that can track fast SOP changes by using multiple threads to implement filter convolution. It tracks faster SOP changes with the same filter update cycle as the conventional method by calculating multiple filters on multiple threads and averaging them. Its graphics processing unit (GPU) implementation adopts parallel reduction for averaging the filters and parallelizes the update of filters for tributaries X and Y in order to take advantage of many GPU threads. We evaluate the real-time bit error rate (BER) performance achieved by implementing the DSP functions with the proposal in a 5-Gb/s dual-polarization-quadrature phase-shift keying (DP-QPSK) system. The demonstration results show that the proposed algorithm supports 2,000 rd/s real-time performance for optical systems and offers high feasibility for application to the fully softwarized access network.

关键词： Instruction sets Equalizers Demultiplexing Software Graphics processing units signal processing algorithms Optical network units NFV PON SDN Softwarization and polarization demultiplexing

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VISION-ASSISTED MILLIMETER-WAVE BEAM MANAGEMENT FOR NEXT-GENERATION WIRELESS SYSTEMS

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IEEE VEHICULAR TECHNOLOGY MAGAZINE 2023年第3期18卷 58-68页

作者： Zheng, Kan Yang, Haojun Ying, Ziqiang Wang, Pengshuo Hanzo, Lajos Ningbo Univ Ningbo 315211 Peoples R China Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Beijing Univ Posts & Telecommun Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Informat & Commun Engn Beijing 100876 Peoples R China Univ Southampton Sch Elect & Comp Sci Southampton England

Beamforming techniques have been widely used in the millimeter-wave (mm-wave) bands to mitigate the path loss of mm-wave radio links as narrow straight beams by directionally concentrating the signal energy. However, traditional mm-wave beam management algorithms usually require excessive channel state information (CSI) overhead, leading to extremely high computational and communication costs. This hinders the widespread deployment of mm-wave communications. By contrast, the revolutionary vision-assisted beam management system concept employed at base stations (BSs) can select the optimal beam for the target user equipment (UE), based on location information determined by machine learning (ML) algorithms applied to visual data, without requiring channel information. In this article, we present a comprehensive framework for a vision-assisted mm-wave beam management system, its typical deployment scenarios, and the specifics of the framework. Then, some of the challenges faced by this system and their efficient solutions are discussed from the perspective of ML. Next, a new simulation platform is conceived to provide both visual and wireless data for model validation and performance evaluation. Our simulation results indicate that vision-assisted beam management is indeed attractive for next-generation wireless systems.

关键词： Wireless communication Detectors Computational modeling Cameras Predictive models Visualization signal processing algorithms

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Quickest Anomaly Detection in Sensor Networks With Unlabeled Samples

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 873-887页

作者： Sun, Zhongchang Zou, Shaofeng Univ Buffalo Dept Elect Engn Buffalo NY 14228 USA

The problem of quickest anomaly detection in networks with unlabeled samples is studied. At some unknown time, an anomaly emerges in the network and changes the data-generating distribution of some unknown sensor. The data vector received by the fusion center at each time step undergoes some unknown and arbitrary permutation of its entries (unlabeled samples). The goal of the fusion center is to detect the anomaly with minimal detection delay subject to false alarm constraints. With unlabeled samples, existing approaches that combines local cumulative sum (CuSum) statistics cannot be used anymore. Several major questions include whether detection is still possible without the label information, if so, what is the fundamental limit and how to achieve that. Two cases with static and dynamic anomaly are investigated, where the sensor affected by the anomaly may or may not change with time. For the two cases, practical algorithms based on the ideas of mixture likelihood ratio and/or maximum likelihood estimate are constructed. Their average detection delays and false alarm rates are theoretically characterized. Universal lower bounds on the average detection delay for a given false alarm rate are also derived, which further demonstrate the asymptotic optimality of the two algorithms.

关键词： signal processing algorithms Delays Trajectory Heuristic algorithms Sun Maximum likelihood estimation Change detection algorithms Quickest change detection unlabeled samples permuted samples asymptotically optimal fundamental limits

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Secure Distributed Optimization Under Gradient Attacks

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1802-1816页

作者： Yu, Shuhua Kar, Soummya Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

In this article, we study secure distributed optimization against arbitrary gradient attacks in multi-agent networks. In distributed optimization, there is no central server to coordinate local updates, and each agent can only communicate with its neighbors on a predefined network. We consider the scenario where out of $n$ networked agents, a fixed but unknown fraction p of the agents are under arbitrary gradient attacks in that their stochastic gradient oracles return arbitrary information to derail the optimization process, and the goal is to minimize the sum of local objective functions on unattacked agents. We propose a distributed stochastic gradient method that combines local variance reduction and clipping (CLIP-VRG). We show that, in a connected network, when the unattacked local objective functions are convex and smooth, share a common minimizer, and their sum is strongly convex, CLIP-VRG leads to almost sure convergence of the iterates to the exact sum cost minimizer at all agents. We quantify a tight upper bound on the fraction p of attacked agents in terms of problem parameters such as the condition number of the associated sum cost that guarantee exact convergence of CLIP-VRG, and characterize its asymptotic convergence rate. Finally, we empirically demonstrate the effectiveness of the proposed method under gradient attacks on both synthetic and real-world image classification datasets.

关键词： Optimization Stochastic processes Distributed databases Servers Threat modeling Robustness signal processing algorithms Distributed optimization multi-agent networks security resilience gradient descent variance reduction

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The Discriminative Discrete Basis Problem: Definitions, algorithms, Benchmarking, and Application to Brain's Functional Dynamics

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 1-16页

作者： Haddad, Ali E. E. Najafizadeh, Laleh Univ Basrah Dept Comp Engn Basrah 61004 Iraq Rutgers State Univ Dept Elect & Comp Engn Piscataway NJ 08854 USA

In this paper, considering two Boolean matrices, we tackle the problem of finding a set of Boolean basis vectors that capture the latent components most discriminative of one matrix from another. We refer to this problem as the discriminative discrete basis problem (DDBP). Depending on the symmetry/asymmetry of the problem about the two input matrices, different classes of the DDBP are defined. We present an ensemble of new algorithms, called the discriminative-associative (DASSO) algorithms, to find solutions for these classes of the DDBP. Furthermore, we design a comprehensive, simulation-based benchmarking framework for evaluating the performance of such algorithmic solutions to the DDBP over a wide range of variables, including the effects of disturbances, noise, the geometric features of the latent components within each matrix, and the relationships among these components across the contrasted matrices. As an example application, we demonstrate the utility of the DASSO algorithms in studying the dynamics of the brain function by identifying the cortical activities that discriminate motor from non-motor tasks using recordings obtained via electroencephalography (EEG). The results highlight the location of such discriminating activities in the cortex and the time of their occurrence, which can lead to the design of efficient and accurate brain computer interfaces (BCIs). To the best of our knowledge, this is the first time the DDBP is recognized, defined, and addressed through the development of algorithmic solutions. The DASSO algorithms can be utilized in a variety of data mining and feature selection applications, in which finding discriminating structures across two datasets is of interest.

关键词： signal processing algorithms Matrix decomposition Heuristic algorithms Approximation algorithms Task analysis Electroencephalography Data mining Boolean matrix factorization (BMF) data mining feature selection brain-computer interfaces (BCIs) dynamic functional networks electroencephalography (EEG)

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Enhanced Graph-Learning Schemes Driven by Similar Distributions of Motifs

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IEEE TRANSACTIONS ON signal processing 2023年 71卷 3014-3027页

作者： Rey, Samuel Roddenberry, T. Mitchell Segarra, Santiago Marques, Antonio G. King Juan Carlos Univ Dept Signal Theory & Commun Madrid 28933 Spain Rice Univ Dept Elect & Comp Engn Houston TX 77251 USA

This paper looks at the task of network topology inference, where the goal is to learn an unknown graph from nodal observations. One of the novelties of the approach put forth is the consideration of prior information about the density of motifs of the unknown graph to enhance the inference of classical Gaussian graphical models. Directly dealing with the density of motifs constitutes a challenging combinatorial task. However, we note that if two graphs have similar motif densities, one can show that the expected value of a polynomial applied to their empirical spectral distributions will be similar. Guided by this, we first assume that we observe a reference graph with a density of motifs similar to that of the sought graph, and then, we exploit this relation by incorporating a similarity constraint and a regularization term in the graph learning optimization problem. The (non-)convexity of the optimization problem is discussed, and a computationally efficient alternating majorization-minimization algorithm is designed. We assess the performance of the proposed method through exhaustive numerical experiments, where different constraints are considered and compared against popular alternatives on both synthetic and real-world datasets.

关键词： Topology Network topology signal processing algorithms Task analysis Optimization Mathematical models Graphical models Network topology inference graphical models graph signal processing motif distribution

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Anomaly Detection Based on Tree Topology for Hyperspectral Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2023年 16卷 1985-2008页

作者： Sun, Xiaotong Zhang, Bing Zhuang, Lina Gao, Hongmin Sun, Xu Ni, Li Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Computat Opt Imaging Technol Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Beijing 100094 Peoples R China Hohai Univ Coll Comp & Informat Nanjing 211100 Peoples R China

As one of the most important research and application directions in hyperspectral remote sensing, anomaly detection (AD) aims to locate objects of interest within a specific scene by exploiting spectral feature differences between different types of land cover without any prior information. Most traditional AD algorithms are model-driven and describe hyperspectral data with specific assumptions, which cannot combat the distributional complexity of land covers in real scenes, resulting in a decrease in detection performance. To overcome the limitations of traditional algorithms, a novel tree topology based anomaly detection (TTAD) method for hyperspectral images (HSIs) is proposed in this article. TTAD departs from the single analytical mode based on specific assumptions but directly parses the HSI data itself. It makes full use of the "few and different " characteristics of anomalous data points that are sparsely distributed and far away from high-density populations. On this basis, topology, a powerful tool in mathematics that successfully handle multiple types of data mining tasks, is applied to AD to ensure sufficient feature extraction of land covers. First, the redistribution of HSI data is realized by constructing a tree-type topological space to improve the separability between anomalies and backgrounds. Then, topologically related subsets in this space are utilized to evaluate the abnormality degree of each sample in a dataset, and detection results for the HSI are output accordingly. Abandoning traditional modeling but focusing on mining the data characteristics of HSI itself enables TTAD to better adapt to different complex scenes and locate anomalies with high precision. Experimental results on a large number of benchmark datasets demonstrate that TTAD could achieve excellent detection results with considerable computational efficiency. The proposed method exhibits superior comprehensive performance and is promising to be popularized in practical applicat

关键词： Anomaly detection Topology Hyperspectral imaging Mathematical models Task analysis signal processing algorithms Feature extraction Anomaly detection (AD) data mining hyperspectral remote sensing machine learning (ML) topological mapping

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