Machine Learning Paradigms for Next-Generation Wireless Networks

By Chunxiao Jiang, Haijun Zhang, Yong Ren, Zhu Han, Kwang-Cheng Chen, Lajos Hanzo. IEEE wireless communications, 2017

1,005 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/dd41d656e21c30dd761bee2eba303d1aa014d120

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design (2021, background): “non-trivial problems due to extremely large dimension in large scale MIMO systems [53], [54].”
Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “Third, a large number of the existing surveys, such as [2], [4], and [52]–[57], are highly qualitative and do not provide an in-depth technical and quantitative description on the variety of existing machine learning tools that are suitable for wireless communications.”
Massive MIMO is a Reality - What is Next? Five Promising Research Directions for Antenna Arrays (2019, background): “There is a recent surge of papers applying Machine Learning (ML) to various problems in communications [140].”
Machine Learning for Vehicular Networks: Recent Advances and Application Examples (2017, methodology): “It develops efficient methods to analyze a huge amount of data by finding patterns and underlying structures, which can be beneficial to supporting future smart radio terminals [5].”
A Comprehensive Survey on Internet of Things (IoT) Toward 5G Wireless Systems (2020, neutral): “3 [4], [33], [58], [62], [71], [83], [86], [99], [117].”
Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions (2019, methodology): “Some authors highlighted the potential of ML as an enabler for cellular networks [18], networking functions [19], or radio communications [20].”
Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning (2020, neutral): “6G and beyond) [31]– [40].”
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks (2019, methodology): “This is achieved by accurately learning the near-real-time physical operating scenario, which allows them to outperform the traditional model-driven optimization algorithms based on more-restrictive assumptions detailed in [16].”
Wireless Networks Design in the Era of Deep Learning: Model-Based, AI-Based, or Both? (2019, background): “As far as communications are concerned, most previous surveys discuss general machine learning techniques [14], [17], [18], [38]–[40], without providing a dedicated analysis of deep learning.”
Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “Intelligent mobile networking is becoming a popular research area and related work has been reviewed in [7], [34], [37], [54], and [56]–[59].”
A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective (2018, background): “[144] reviewed and investigated a variety of machine learning techniques (e.”
End-to-End Deep Learning of Optical Fiber Communications (2018, background): “THE application of machine learning techniques in communication systems has attracted a lot of attention in recent years [1], [2].”
Big Data Analytics, Machine Learning, and Artificial Intelligence in Next-Generation Wireless Networks (2017, background): “While ML is excellent for predictive analytics [10], AI goes beyond predictions and prescribe plans/suggestions with implications to realize a beneﬁt.”
End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications (2017, methodology): “We refer to this process as spectrum learning [27].”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, positive): “They can be used for estimating the mobile users’ decision making under unknown scenarios, such as channel access under unknown channel availability conditions in spectrum sharing, base station association under the unknown energy status of the base stations in energy harvesting networks, and distri”
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues (2018, methodology): “In [19], the basics of some machine learning algorithms along with their applications in future wireless networks are introduced, such as Q-learning for the resource”