Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey

In plain words

Optical networks carry most long-distance internet traffic over glass fibre. Their signals degrade in many ways, and the routes, wavelengths and capacity that carry traffic have to be chosen under changing conditions. Exact mathematical models of these problems can be slow to solve, or they need information that is not available.

This review asks where artificial intelligence has helped. It covers two broad areas: the physical signal and the equipment that sends it, and the planning and control of the network that routes it. For each technique it names example studies, so a reader can see which methods have been tried on which problem. It also points to areas it thinks are still open.

TL;DR

A 2018 review, not a method. It groups AI techniques (search and optimisation, probabilistic models, decision processes, machine learning) and maps each to optical transmission and networking work, in three tables tab. 1 tab. 2 tab. 3. Use it to find which technique has been applied to which problem. Do not take its numbers as evidence for a design: they are claims of the cited studies, and the text reports no experiments of its own.

Problem

The review addresses optical systems where the conditions are not fully known, change over time, or are too costly to model exactly. It names noise, nonlinear distortion and time-varying impairments in transmission §3.4 §3.5, and in networks, planning and routing problems whose exact solution grows hard with network size §2 §4.2. Analytical or semi-analytical models are cited as too slow or too rigid in several places §3.5 §6.

Method

This paper has no method to implement and no algorithm, pseudocode or equations. Its structure is:

1. Introduction and scope §1.
2. Background on AI: six subfields, each with examples from optical work. Figure 1 is a diagram that places the cited studies in these categories fig. 1. The subfields are:
   • search and optimisation: exact search and linear or mixed-integer programming first, then metaheuristics such as simulated annealing, genetic algorithms, swarm methods and teaching-learning optimisation §2;
   • game theory, for several agents whose actions affect each other §2;
   • knowledge, reasoning and planning, as in cognitive optical networks §2;
   • uncertainty and temporal models: Bayesian networks, hidden Markov models and Kalman filters §2;
   • decision making: maximum expected utility and Markov decision processes §2;
   • learning: Bayesian, maximum a posteriori and maximum-likelihood learning, then supervised, unsupervised and reinforcement learning, including Q-learning §2.
3. Applications in optical transmission §3 tab. 1. Subsections cover transmitters and lasers §3.1, erbium-doped fibre amplifiers §3.2, performance monitoring §3.3, receivers and nonlinearity mitigation §3.4, and quality-of-transmission (QoT) estimation §3.5.
4. Applications in optical networking §4 tab. 2 tab. 3. Subsections cover planning §4.1, connection establishment §4.2, virtual topology reconfiguration §4.3, software-defined networking §4.4, optical burst switching §4.5, passive optical networks §4.6 and intra-datacentre networks §4.7.
5. Opportunities and challenges §5: transmission and attack detection §5.1, automating network management §5.2, joint operation of networks and computing §5.3, and on-chip networks §5.4.
6. Summary §6.

The survey's own framing of which family fits which scenario, restated as a sketch (not an algorithm the paper gives) §2:

# Choose the AI family the survey associates with a problem  [§2]
# Input:  properties of the optical problem (flags)
# Output: name of the technique family the survey points to

def suggest_family(problem):
    # Deterministic, observable, static and fully known  [§2]
    if problem.fully_known_and_static:
        return "search and optimisation theory"
    # Conditions relaxed, or network too large for exact methods  [§2]
    if problem.too_large_or_relaxed:
        return "local search and metaheuristics"
    # Several agents whose actions affect each other  [§2]
    if problem.multi_agent:
        return "game theory"
    # Uncertainty about the environment  [§2]
    if problem.uncertain:
        return "Bayesian networks"
    # Changing state over time, filtering or prediction  [§2]
    if problem.temporal_state:
        return "hidden Markov models and Kalman filters"
    # Sequential decisions where only the current state matters  [§2]
    if problem.sequential_decisions:
        return "Markov decision processes"
    # Otherwise learn from data  [§2]
    return "machine learning"

Transmission table (condensed from tab. 1)

Networking tables (condensed from tab. 2 and tab. 3)

Hyperparameters

Not applicable: no model is trained or released by this paper. The only training settings stated are those of one cited study, reported as a requirement of that study:

Results

Author-reported in the sense that the survey repeats the claims of other authors. No experiment is run by this paper. Setup, baseline and metric are stated only where the text gives them.

Paper vs code

No official code. The paper is a review with no implementation, and its LaTeX source links no repository or project page.

Pitfalls

• Dated snapshot. The arXiv version is v2 (2018-01-15). The reference list ends around 2017 §1. Statements such as attack detection having not "yet emerged" in optical networks §5.1 describe the field in 2017–2018 only.
• No selection method. The text gives no search protocol or inclusion rule for the works it covers. Coverage is therefore not reproducible from the paper. Not stated.
• Self-citation. The survey's first author, Javier Mata, is also the first author of the SVM QoT study it describes as faster and more accurate than a case-based approach §3.5. The comparison comes from that study and is not independently checked here.
• Table and text disagree. In the burst-loss rows of tab. 2, the study by Jayaraj et al. (key jayaraj2008loss) is listed under hidden Markov models with EM. The text describes the same study as TCP-protocol variants using supervised and unsupervised learning §4.5. Check the primary paper before citing either description.
• Author-name typos. The text names "Hragui et al." §3.1; the citation key and bibliography say Hraghi (hraghi2017demonstration). The text names "Xiang et al." §4.2; the bibliography lists Y. Xiong (xiong2017power).
• Claims carry no baseline. Percentages and speed-ups in §3.5, §4.2 and §4.3 are given without the full setup. Treat "up to" values as ceilings.
• No algorithm detail. Nothing here is enough to implement a method. For any technique in the tables, go to the cited study and check it there.
• Figure only in words. Figure 1 is a paper figure, so it is described in words fig. 1 and not reproduced.

Lineage

• Builds on: nothing specific. The review draws on textbooks and the optical-networking literature it cites §2.
• Compared against: no other survey is named in the text.
• Superseded by: not known from content already in this wiki.
• Publication venue, from the LaTeX source: Optical Switching and Networking.