Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey

By Qian Mao, Fei Hu, Qi Hao. 2018

717 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning (2018, methodology): “, a typical 5G node is expected to have 2000 or more configurable parameters, a recent new trend is to optimize wireless communication by Artificial Intelligence (AI) techniques [4] [5], include but not limited to the application of AI for Physical Layer (PHY), Data Link Layer, and traffic control [”
The Future of Healthcare Internet of Things: A Survey of Emerging Technologies (2020, methodology): “At the higher layers of the protocol stack, ML performs tasks like resource management, data processing, and application optimization [7].”
Edge-Computing-Enabled Smart Cities: A Comprehensive Survey (2019, neutral): “in [166] to use AI for data link and physical layer.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, neutral): “ne learning in mobile and wireless networking, also in MEC and other edge computing paradigms, are illustrated in Fig 14. Recently, some surveys and tutorials on machine learning [339], deep learning [340], (deep) reinforcement learning [341]–[343], as well as their applications in communications an”
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence (2019, background): “Some works also study the concept from the perspective of AI-driven fog computing [6], [7].”
6G Wireless Networks: Vision, Requirements, Architecture, and Key Technologies (2019, background): “AI-enabled resource allocation, with or without traffic prediction [2], can choose the most suitable scheduling for users.”
What should 6G be? (2019, methodology): “With the development of machine learning techniques, especially deep learning, a BS equipped with graphics processing units (GPUs) or the control centre of core network could carry out relevant learning algorithms to allocate resources efficiently to achieve performance close to the optimum [68].”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “focus on deep learning in wireless networking [68].”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, methodology): “32], and [50]–[62].Nevertheless, these works are limited in a number of ways. First,a majority of the existing worksfocuses on a single ML technique (often the basics of deep learning [32], [50], and [56]–[58] or reinforcement learning [61]) and, as such, they do not capture the rich spectrum of ava”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “control, the survey in [11] presents deep learning approaches (a) (b)”
Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges (2021, background): “The authors of [148] list several promising studies on using deep learning models to overcome the challenges as”
Joint Design of Communication and Sensing for Beyond 5G and 6G Systems (2021, background): “The recent advances in artiﬁcial intelligence (AI) and machine learning (ML) for communication systems [25] pave the way for AI/ML to become an integral part of the design of beyond 5G (B5G) and 6G systems (e.g. [3], [5]).”
A Tutorial on Ultrareliable and Low-Latency Communications in 6G: Integrating Domain Knowledge Into Deep Learning (2021, background): “Mao et al. [82] reviewed how to use deep learning in different layers of the OSI model and cross-layer design.”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “First, a majority of the existing works focuses on a single ML technique (often the basics of deep learning [32], [50], and [56]–[58] or reinforcement learning [61]) and, as such, they do not capture the rich spectrum of available ML frameworks.”
Wireless Edge Computing With Latency and Reliability Guarantees (2019, background): “Edge Machine Learning : Inference (or prediction) capabilities with low latency is one of the main reason for ML to be popular in MEC as well as several other communication applications such as coding, beamforming, resource optimization, caching, scheduling, routing, and security [43]–[46].”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, background): “Many works focus on applying DL in wireless communication [265]–[267].”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, background): “In addition, only a specific network scenario is covered in [20]–[24], while literatures [28], [31]–[33]”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, background): “…of deep learning in wireless multimedia network applications, including ambient sensing, cyber-security, resource optimization, etc. Mao et al. [43] provided a comprehensive survey of the applications of deep learning algorithms in terms of different network layers, including physical layer,…”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “In 5G, the superior performance has been achieved by leveraging the AI capability into RAN [289], [290].”