A Survey on Machine and Deep Learning for Optical Communications

Lawrence R. Chen; M. A. Amirabadi; M. H. Kahaei; S. A. Nezamalhosseini

REVIEW 4 major objections 5 minor 283 references

A Survey on Machine and Deep Learning for Optical Communications

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read The survey claims that 282 prior studies, organized by algorithm, reveal where machine learning and deep learning improve optical fiber, network, and wireless systems over conventional baselines.

desk verdict A useful, algorithm-centric survey of ML/DL in optical communications whose quantitative tables are inconsistent enough that they need an audit before the paper can be trusted. read the letter →

arxiv 2412.17826 v1 pith:NFNQQ2KD submitted 2024-12-10 eess.SP

M. A. Amirabadi , S. A. Nezamalhosseini , M. H. Kahaei , Lawrence R. Chen This is my paper · ORCID

classification eess.SP

keywords machinelearningdeepopticalfibercommunicationnetworkingwirelessperformancemonitoringnonlinearequalizationreinforcement

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper is a survey: it tries to establish, in one place, how machine learning and deep learning are being used across optical fiber communication, optical communication networking, and optical wireless communication. It organizes roughly 282 prior studies into an algorithm-first taxonomy, and it summarizes their reported performance, complexity, data, and metrics in six comparison tables. A sympathetic reader would care because the survey turns a scattered literature into a map: for each type of task, it shows which algorithm families have been tried and what quantitative gains they claim over conventional methods. The paper further argues that deep learning is underexplored relative to machine learning, that optical wireless communication has been neglected by earlier surveys, and that algorithm-centric comparison reveals gaps and future directions.

What carries the argument

The organizing machinery is a two-level taxonomy: three application domains, namely optical fiber communication, optical communication networking, and optical wireless communication, each subdivided into machine learning (supervised, unsupervised, and reinforcement learning) and deep learning (DNN, RNN, CNN, and DRL). Six summary tables, Tables III through VIII, carry the argument; each row reports performance, complexity, train/test data type, objective, input features, metrics, and adopted algorithm for the reviewed studies. This structure is what lets the survey compare quantitative gains across heterogeneous setups.

What would settle it

Randomly sample a dozen quantitative entries in Tables III through VIII, read the original papers behind the cited markers, and recompute or repeat each measurement under the stated conditions; finding that the reproduced values or their experimental conditions diverge materially from the table entries would undercut the survey's comparative conclusions.

Watch

Extended reading notes

Core claim

The paper's central claim is that the literature on machine learning and deep learning for optical communication can be organized by algorithm rather than only by application, and that doing so exposes consistent patterns. Supervised and unsupervised learning methods, including support vector machines, artificial neural networks, k-nearest neighbors, clustering, principal and independent component analysis, regression, and ensemble learning, are widely used for nonlinear equalization, detection, quality-of-transmission estimation, and optical performance monitoring. Deep architectures, including deep neural networks, recurrent networks, convolutional networks, and deep reinforcement learning, push reported performance further in many cases while often claiming lower complexity than classical nonlinear equalizers. The survey asserts, with quantitative entries in Tables III through VIII, that these approaches improve Q-factor, bit error rate, OSNR estimation accuracy, spectrum utilization, blocking probability, throughput, and positioning error relative to conventional baselines. The discovery, if the survey is right, is not any single algorithm but the landscape itself: which algorithm families are attached to which optical tasks, and where the reported gains are largest.

Load-bearing premise

The position rests on the assumption that the performance numbers drawn from the cited studies are accurate and can be fairly compared across different simulators, fiber lengths, modulation formats, and data rates.

Editorial extensions

If this is right

For nonlinear equalization in fiber links, the tables show several viable families, including SVM, ANN, clustering, RNN, CNN, and DRL-optimized Volterra equalizers, so a system designer can trade reported performance against complexity.
For monitoring and quality-of-transmission estimation, image-based CNN and feature-based RF/DNN methods report high accuracy for joint OSNR and modulation-format identification, supporting consolidation of several monitoring functions into one model.
For network-level control, RL and DRL agents are reported to reduce blocking probability and improve throughput or spectrum utilization in routing, spectrum assignment, handover, and power allocation problems.
The survey's future-directions argument implies that continual learning, transfer learning, active learning, explainable AI, and open datasets are the next needed steps if these methods are to be deployed in dynamic optical networks.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the reported quantitative gains are taken at face value, the strongest cross-cutting pattern is that hybrid methods, such as DNN/RNN cascades for equalization and CNN/RF for monitoring, appear most often; this suggests a design heuristic the paper does not state explicitly.
The comparability problem in the tables is the main editorial risk; a standardized benchmark suite with common fiber lengths, modulation formats, and link setups would sharpen the comparisons the survey can only approximate.
The algorithm-centric map highlights gaps, for instance the sparse use of some unsupervised methods in optical networking and optical wireless communication relative to optical fiber communication; these gaps are candidate targets for the next wave of applications.
A testable extension would be to use the survey's table entries as training data for a meta-analysis, regressing reported gains against algorithm family, data rate, distance, and simulation-versus-experiment status to see which factors predict success.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The survey does one thing well: it organizes a large, scattered literature into a clear algorithm-by-application taxonomy, and it is one of the few reviews to give OWC full coverage alongside OFC and OCN. For someone entering this area, the structure and the 282-reference bibliography are genuinely useful. The tables that summarize reported performance, complexity, and data type per paper are a good idea, and they do give a quick sense of what has been tried.

But the stress-test note is right: the tables are not reliable as printed. [104] is described in the text as achieving 99.95% accuracy for QoT estimation, but Table V says 95–99%. [174] appears as MAE 0.18 dB in Table VI and 0.05 dB in the text. [131] is a fault-localization study that Section III treats as part of OCN, yet it sits in the OFC table. Fig. 1 has empty reference slots for several branches (PCA, policy/value RL in OFC; hierarchical and ICA in OCN). These are not merely typos: the paper's stated contribution is "quantitative/qualitative comparisons," and if the numbers and placement are off in the few spots I spot-checked, I cannot trust the rest. The blame is not that the authors invented results; the entries presumably come from the cited papers. But the survey does not provide its search protocol or inclusion criteria, so selection bias is also unquantifiable.

There are also smaller editorial problems — "Deap Learning," "hese," repetitive future-directions prose — that indicate a rushed preprint. None of this changes the taxonomy, which is sound. The core idea — organize by ML/DL family, cross three application domains, list reported gains — is a legitimate and needed service. No new methods or data are claimed, so there is no circularity issue.

I would send this to peer review, but with a clear demand: audit every table entry against its source, fix the internal contradictions, fill or remove the blank slots in Fig. 1, and state the search and selection methodology. After that, the survey becomes a solid entry point. As printed, it is a good skeleton with unreliable flesh.

I would not cite the tables in my own work without checking original sources, but I would point people to the paper for its coverage and organization. It deserves a serious referee, not a desk reject.

Referee Report

4 major / 5 minor

Summary. This manuscript surveys applications of machine learning (ML) and deep learning (DL) in three optical communication domains: optical fiber communication (OFC), optical communication networking (OCN), and optical wireless communication (OWC). It organizes roughly 282 cited works according to ML/DL algorithm families, provides schematic figures of each algorithm type, and presents six comparison tables (Tables III–VIII) that summarize performance, complexity, train/test data type, objective, input features, metrics, and adopted algorithms. The paper also reviews motivation, discusses challenges, and outlines future research directions.

Significance. If the comparison tables are faithful to the cited sources, this survey would provide a useful, up-to-date, algorithm-centric map of ML/DL in optical communications, with broader domain coverage than several prior surveys, particularly through its inclusion of OWC. The organizational contribution is real: grouping by algorithm rather than only by application helps readers compare methods. However, the survey's central value is explicitly claimed to be its quantitative/qualitative comparison tables, so the reliability of those tables is load-bearing. The internal inconsistencies identified below directly affect that reliability and must be resolved before the survey can serve its stated purpose.

major comments (4)

[Section III.A / Table V, row [104]] The text states that the SVM-based QoT estimator in [104] achieves 99.95% accuracy in lightpath classification, while Table V reports 95-99% accuracy for the same reference. These numbers are mutually incompatible. Because Table V is one of the key quantitative comparison tables that the paper presents as a contribution, this discrepancy is not cosmetic. Please correct the entry and audit other table-text pairs for the same reference; for example, reference [133] is reported in Table V as detecting 95% of malicious nodes, while the text in Section III.A reports up to 90% accuracy for the K-means-based scheme.
[Table III, row [131] / Section III.A] Reference [131] is listed in Table III as an ML application in OFC, with a fault-localization objective based on optical time-domain reflectometry measurements, but the text discusses the same work in Section III.A under OCN regression algorithms. This is a domain misplacement in the paper's central taxonomy. Since the survey's contribution includes a clear algorithm/domain classification, the placement must be corrected and other table entries checked for similar cross-domain inconsistencies.
[Table VI, row [174] / Section III.B] Table VI reports for reference [174] a mean absolute error of 0.18 dB for OSNR estimation, while the text in Section III.B states that the same reference achieved a MAE of 0.05 dB. Both cannot be correct for the same reported result. The discrepancy changes the stated performance gain and undermines confidence in the quantitative summaries; please reconcile the entry against the cited work.
[Fig. 1] Several branches of the taxonomy have empty reference slots, including PCA in OFC, policy-based and value-based RL in OFC, ensemble learning in OFC, hierarchical clustering in OCN, ICA in OCN, and PCA in OWC. If no works exist in these categories, the survey should state that explicitly; if works were omitted, the 'comprehensive' claim is weakened. As printed, the unexplained gaps leave the central organizational figure incomplete.

minor comments (5)

[Section II.B heading] The heading 'Deap Learning' contains a typo; it should be 'Deep Learning'.
[Section IV.A] The paragraph beginning 'hese RL-based approaches...' is missing the initial T; it should be 'These RL-based approaches'.
[Fig. 13] The text 'Receive Rewrard and New State' contains a typo; it should be 'Receive Reward and New State'.
[Section I.C] The phrase 'OFC, OCW, and OCN' uses 'OCW', which is not defined in the acronym list; this should be 'OWC'.
[Table I] The acronym table lists both 'Principal Component PC' and 'Principal Component Analysis PCA'; this is redundant and potentially confusing.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the paper is a survey that summarizes external results, and its claimed contribution is organizational rather than a derivation or prediction fitted to its own inputs.

full rationale

The manuscript is a survey paper with no derivation chain, fitted parameters, or first-principles predictions. Its stated contributions are (1) an up-to-date taxonomy of ML/DL applications in OFC, OCN, and OWC, (2) summaries of roughly 282 external studies, and (3) quantitative/qualitative comparison tables. Each Section II–IV describes algorithms and then reports results cited from prior work; no equation is derived from a premise that relies on the conclusion, and no fitted quantity is renamed as a prediction. The paper does self-cite the authors' own previous surveys in Table II, but that self-citation is used only to position the survey relative to prior reviews, not to justify a load-bearing technical claim. The skeptical concern about internally inconsistent table entries (e.g., Table V row [104] reporting 95–99% accuracy while Section III.A states 99.95%; Table VI row [174] reporting 0.18 dB MAE while the text reports 0.05 dB) is a correctness/fidelity risk in the survey's secondary reporting, not a circularity of derivation. Because the survey's content is organizational and externally sourced, the circularity score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities appear because the survey contributes no new mathematical derivation. Its conclusions rest on the accuracy and representativeness of the cited literature, which is an external assumption rather than a falsifiable handle.

assumptions (3)

domain assumption Reported performance gains in the surveyed papers are accurate as cited.
The survey's tables and comparisons inherit the numbers from the cited works without independent verification (Tables III-VIII).
domain assumption The ML/DL taxonomy (SL/USL/RL and DNN/RNN/CNN/DRL) is a meaningful organizational structure for the literature.
The paper's review structure depends on this categorization; alternative taxonomies could organize the same papers differently (Sections I-D and II-IV).
domain assumption The cited set of 282 papers is representative enough to support the claim of a comprehensive survey.
No systematic search protocol or inclusion criteria are described (Section I-B).

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Cite this review

Pith. "Pith review of A Survey on Machine and Deep Learning for Optical Communications." pith.science (2026). https://pith.science/paper/NFNQQ2KD

@misc{pith2026241217826,
  author       = {Pith},
  title        = {Pith review of: A Survey on Machine and Deep Learning for Optical Communications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NFNQQ2KD}},
  note         = {Machine review of arXiv:2412.17826}
}

read the original abstract

The ever-growing complexity of optical communication systems and networks demands sophisticated methodologies to extract meaningful insights from vast amounts of heterogeneous data. Machine learning (ML) and deep learning (DL) have emerged as frontrunners in this domain, offering a transformative approach to data analysis and enabling automated self-configuration in optical communication systems. The adoption of ML and DL in optical communication is driven by the exponential increase in system and link complexity, stemming from the introduction of numerous adjustable and interdependent parameters. This is particularly evident in areas like coherent transceivers, advanced digital signal processing, optical performance monitoring, cross-layer network optimizations, and nonlinearity compensation. While the potential benefits of ML and DL are immense, the extent to which these methods can revolutionize optical communication remains largely unexplored. Additionally, many ML and DL algorithms have yet to be deployed in this field, highlighting the nascent nature of this research area.

Figures

Figures reproduced from arXiv: 2412.17826 by the authors.

**Figure 2.** Descriptions of SVM classifier and regressor. [PITH_FULL_IMAGE:figures/full_fig_p011_2.png] view at source ↗

**Figure 3.** Schematic of an ANN. with constant module algorithm (CMA) equalizers [39], [44] to enhance the performance of high-density carrier less amplitude phase (CAP) modulation. This approach demaps the rotated constellations directly without any correction, significantly reducing bit error rate (BER) compared to traditional hard decision methods [45]. Nonlinear equalizers (NLEs) is a crucial technique for compensating for … view at source ↗

Figures from the paper (14 more)

**Figure 4.** Figure 4: Visualization of kNN classification procedure. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png]

**Figure 5.** Figure 5: An example of linear regression algorithm. [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]

**Figure 6.** Figure 6: Demonstration of hierarchical clustering. [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]

**Figure 7.** Figure 7: Example of K-means clustering. erful tool for addressing signal distortion and enhancing performance in various optical communication applications. Its ability to identify patterns and group data points based on their similarities makes it well-suited for mitigating fi…

**Figure 8.** Figure 8: Example of EM clustering. PCA ICA x1 x2 x1 x2 PC2 PC1 IC1 IC2 [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]

**Figure 9.** Figure 9: Example of data fitted using PCA and ICA. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]

**Figure 10.** Figure 10: Schematic of DNN. and signal components, ICA can effectively mitigate the effects of PMD and PDL in coherent optical communication systems. In [77], ICA is employed for blind equalization and phase recovery in coherent transmission. Simulation results show that ICA ac…

**Figure 11.** Figure 11: Schematic of RNN. Output layer Input image Convolutional layer Pooling layer Fully connected layer Convolutional layer Pooling layer [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]

**Figure 12.** Figure 12: Structure of CNN algorithm. suited for equalization in optical communication systems due to their ability to learn complex relationships. In [80], DNNs were employed for compensation of fiber linear and nonlinear effects with low complexity. This approach effectively …

**Figure 13.** Figure 13: Schematic of DRL algorithm. SVMs have emerged as powerful tools for QoT estimation and OPM in OCNs. In [104], SVMs were successfully applied for QoT estimation, achieving a remarkable accuracy of 99.95% in lightpath classification. This accuracy surpasses the performa…

**Figure 14.** Figure 14: Schematic of an ensemble learning algorithm. [PITH_FULL_IMAGE:figures/full_fig_p027_14.png]

**Figure 15.** Figure 15: PCA concept illustration. increases to 65.15% and 71.84% for classifying nodes into three categories: behaving, not-behaving, and potentially not-behaving, with 20% and 30% of data, respectively. The application of K-means in optical network deployment and burst heade…

**Figure 16.** Figure 16: Concept of policy-based RL. generation or filtering. Studies [136], [137] demonstrate the efficacy of PCA in identifying abnormal flows or attacks. In fiber optic perimeter intrusion detection systems, PCA can be applied to distinguish intrusions from environmental ev…

**Figure 17.** Figure 17: Concept of value-based RL. allocation, transmit power control, and vertical handover optimization. In [159], QL was employed for resource allocation in LiFi-WiFi access networks supporting the Internet of Things service. The results showed that QL could effectively re…

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