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A Very Brief Introduction to Machine Learning With Applications to Communication Systems
Open accessAbstract
Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modeling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack, with an emphasis on the physical layer.
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Self-citation
Metadata
- Authors
- Simeone, O.
- Affiliations
- King's College London (GB)
- Corresponding author
- Simeone, Osvaldo
- ORCID
- Simeone, Osvaldo
- Venue
- IEEE Transactions on Cognitive Communications and Networking
- Published
- 2018-12
- Volume
- 4
- Issue
- 4
- Pages
- 648-664
- ISSN
- 2332-7731 (ISSN-L)
- 2372-2045
- OpenAlex
- W2885033069
- Cited by
- 516
- References
- 90
- Citations by year
- 2026: 17 · 2025: 40 · 2024: 62 · 2023: 82 · 2022: 76 · 2021: 98 · 2020: 101 · 2019: 39 · 2018: 1
- FWCI
- 37.01
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Transactions on Cognitive Communications and Networking (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- H2020 European Research Council (725731)
- National Science Foundation (1525629)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- machine learning, communication systems, supervised learning, unsupervised learning, physical layer, edge computing, cloud computing, protocol stack, data-driven methods
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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