Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Overview of Machine Learning-aided Optical Performance Monitoring
  Techniques   Future communication systems are faced with increased demand for high
capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet
these requirements, networks have become more complex and thus require new
design methods and monitoring techniques, as they evolve towards becoming
autonomous. Machine learning has come to the forefront in recent years as a
promising technology to aid in this evolution. Optical fiber communications can
already provide the high capacity required for most applications, however,
there is a need for increased scalability and adaptability to changing user
demands and link conditions. Accurate performance monitoring is an integral
part of this transformation. In this paper we review optical performance
monitoring techniques where machine learning algorithms have been applied.
Moreover, since alot of OPM depends on knowledge of the signal type, we also
review work for modulation format recognition and bitrate identification. We
additionally briefly introduce a neuromorphic approach to OPM as an emerging
technique that has only recently been applied to this domain.

30
The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
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The categories of this paper are: mtrl-th
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int