Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks

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

226 citations recorded by the source corpus.

Next-generation wireless networks must support ultra-reliable, low-latency communication and intelligently manage a massive number of Internet of Things (IoT) devices in real-time, within a highly dynamic environment. This need for stringent communication quality-of-service (QoS) requirements as well as mobile edge and core intelligence can only be realized by integrating fundamental notions of artificial intelligence (AI) and machine learning across the wireless infrastructure and end-user devices. In this context, this paper provides a comprehensive tutorial that introduces the main concepts of machine learning, in general, and artificial neural networks (ANNs), in particular, and their potential applications in wireless communications. For this purpose, we present a comprehensive overview on a number of key types of neural networks that include feed-forward, recurrent, spiking, and deep neural networks. For each type of neural network, we present the basic architecture and training procedure, as well as the associated challenges and opportunities. 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 and edge caching.For each individual application, we present the main motivation for using ANNs along with the associated challenges while also providing a detailed example for a use case scenario and outlining future works that can be addressed using ANNs. In a nutshell, this article constitutes one of the first holistic tutorials on the development of machine learning techniques tailored to the needs of future wireless networks.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Network Intelligence at the Edge (2018, methodology): “Basics of ML, its NN architectures and communication designs were recently overviewed in [26], [27].”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “, for caching popular contents, may not benefit from well-established and reliable models, making a data-driven approach desirable (see [26], [27]).”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, background): “[19] presented a comprehensive summary towards the ML applications in wireless networks, including wireless communications and networking using Unmanned Aerial Vehicles (UAVs), wireless virtual reality, mobile edge caching and computing, spectrum management and co-existence of multiple radio access,”
Security analysis of drones systems: Attacks, limitations, and recommendations (2020, unknown): “In fact, Machine Learning can be leveraged in order to design and optimize a smart UAV-based wireless communication system [28] .”
A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, unknown): “[16] have studied”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, background): “, input, hidden, and output layers) [348], the ANN model and the hierarchical MEC architecture are supposed to fit together, where an immediate layer of the entire ANN model can be offloaded to and performed by MEC layers (e.”
Communication Efficient Distributed Learning with Censored, Quantized, and Generalized Group ADMM (2020, methodology): “For the synthetic data, we used the datasets that were generated in Chen et al. (2018). We consider two decentralized consensus optimization problems: (i) linear regression, and (ii) logistic regression.”
Automatic Modulation Classification: A Deep Learning Enabled Approach (2018, background): “The rapid development of deep learning has led to some successful applications in communications [23]–[25], including modulation classification.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “Machine learning can be potentially leveraged to design and optimize UAV-based wireless communication systems [191], [192].”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “Realizing the aforementioned pressing concern has recently sparked huge attention to the introduction of machine learning (ML)-based approaches into communication system designs [12]–[16].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “[70] A tutorial on neural networks for wireless networks.”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “As a randomly generated spiking neural network [82], LSM can store information about the network environment over time”
Collaborative Artificial Intelligence (AI) for User-Cell Association in Ultra-Dense Cellular Systems (2018, background): “Recently, machine learning-based artificial intelligence (AI) techniques [10], [17] have emerged as promising tools that allow a cellular network to leverage the aforementioned and optimize its various cross-layer functions.”
A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems (2019, background): “To perform these critical application tasks, low-latency, high-reliability and scalable AI is needed, along with a reliable infrastructure [9] and [14].”