Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial

By Mingzhe Chen, Ursula Challita, Walid Saad, Changchuan Yin, Merouane Debbah. 2017

871 citations recorded by the source corpus.

In order to effectively provide ultra reliable low latency communications and pervasive connectivity for Internet of Things (IoT) devices, next-generation wireless networks can leverage intelligent, data-driven functions enabled by the integration of machine learning (ML) notions across the wireless core and edge infrastructure. In this context, this paper provides a comprehensive tutorial that overviews how artificial neural networks (ANNs)-based ML algorithms can be employed for solving various wireless networking problems. For this purpose, we first present a detailed overview of a number of key types of ANNs that include recurrent, spiking, and deep neural networks, that are pertinent to wireless networking applications. For each type of ANN, we present the basic architecture as well as specific examples that are particularly important and relevant wireless network design. Such ANN examples include echo state networks, liquid state machine, and long short term memory. Then, we provide an in-depth overview on the variety of wireless communication problems that can be addressed using ANNs, ranging from communication using unmanned aerial vehicles to virtual reality applications over wireless networks as well as edge computing and caching. For each individual application, we present the main motivation for using ANNs along with the associated challenges while we also provide a detailed example for a use case scenario and outline future works that can be addressed using ANNs. In a nutshell, this paper constitutes the first holistic tutorial on the development of ANN-based ML techniques tailored to the needs of future wireless networks.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, methodology): “3) Limitations of Centralized ML: The classical concept of ML focuses mainly on offline and centralized ML [21], [26].”
6G and Beyond: The Future of Wireless Communications Systems (2020, background): “Almost all published works claim significant accuracy or reduced complexity with either analytical theories or practical data sets [107].”
Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach (2021, neutral): “Most recently, the potential of AI technologies has also been exemplified in communication systems [5], [6].”
Artificial Intelligence for the Metaverse: A Survey (2022, methodology): “used at multiple layers of a network architecture [107].”
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks (2019, background): “STANDARD machine learning approaches require centralizing the training data in a data center or a cloud [2]–[4].”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “Due to the rapid evolution of wireless communications and networks, it is believed that artificial intelligence in general and ML in particular will play vital roles in beyond 5G and 6G [233].”
Energy Efficient Federated Learning Over Wireless Communication Networks (2019, background): “To this end, distributed learning frameworks are needed, to enable the wireless devices to collaboratively build a shared learning model with training their collected data locally [6], [8]–[16].”
Evolution of NOMA Toward Next Generation Multiple Access (NGMA) for 6G (2021, background): “ML techniques have gained increasing popularity for resource allocation in heterogeneous wireless networks due to their learning capability, especially in time-varying and unpredictable network environments [190]–[192].”
The Road to 6G: Ten Physical Layer Challenges for Communications Engineers (2020, background): “Promising approaches based on machine learning (ML) and deep learning (DL) have recently been proposed [11].”
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence (2019, background): “Under the circumstances, researchers reach a consensus on that AI technologies should and can be integrated across the wireless infrastructure and mobile users [30].”