A Very Brief Introduction to Machine Learning With Applications to Communication Systems

By Osvaldo Simeone. IEEE Transactions on Cognitive Communications and Networking, 2018

456 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/1c6c0d39d377a465834ed608ae23f48a79ab8eac

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In Artificial Intelligence

In Computer Networks and Communications

In Control and Systems Engineering

Citations in the corpus

White Paper on Broadband Connectivity in 6G (2020, background): “There are two main motivations for this [114]: modeling or algorithmic deﬁciencies.”
Machine Learning in Agriculture: A Comprehensive Updated Review (2021, methodology): “Based on the learning type, ML can be classified according to the relative literature [41,42] as:”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, background): “As such, a data-driven Deep Learning (DL) based approach [28] for optimizing resource allocation is increasingly popular.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “Moreover, some surveys and tutorials on ML, DL, (deep) RL, as well as their applications in communications and networking [230]–[232] have come out, and readers can refer to these literature for more details.”
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “For further readings and insights about supervised learning we refer the readers to [186], [187], [188] and to [189] for deep learning and its applications in wireless communications.”
A Statistical Learning Approach to Ultra-Reliable Low Latency Communication (2018, background): “Addressing these questions calls naturally for the use of the statistical learning methodology, which has recently started to get a traction in the wireless communication community [9].”
Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, background): “For resource management problems, when there is a model deficit or algorithm deficit, ML is recommended [17].”
Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future (2019, background): “In the literature, the stand-alone application of ML and QC to future communication networks has been intensively studied, as summarized in Table 1, see e.g., [11]–[13], [15]–[17], [19], [21], [24], [26], [27] and [30]–[38], respectively.”
Massive MIMO Systems for 5G and beyond Networks—Overview, Recent Trends, Challenges, and Future Research Direction (2020, methodology): “The dynamic nature of machine learning and deep learning algorithms could be instrumental for there complex analysis, and it could save a considerable amount of computational power [145].”
Federated Learning for Wireless Communications: Motivation, Opportunities, and Challenges (2019, methodology): “An alternate to solely utilizing mathematical analyses, such as the ones used in model-based communication system design, is to learn these models using massive amounts of data, which is often available to the network [1].”