Journal ArticleDOI
Machine Learning for Wireless Communication Channel Modeling: An Overview
Saud Aldossari,Kwang-Cheng Chen +1 more
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This paper introduces machine learning to assist channel modeling and channel estimation with evidence of literature survey and shows that machine learning has been successfully demonstrated efficient handling big data.Abstract:
Channel modeling is fundamental to design wireless communication systems. A common practice is to conduct tremendous amount of channel measurement data and then to derive appropriate channel models using statistical methods. For highly mobile communications, channel estimation on top of the channel modeling enables high bandwidth physical layer transmission in state-of-the-art mobile communications. For the coming 5G and diverse Internet of Things, many challenging application scenarios emerge and more efficient methodology for channel modeling and channel estimation is very much needed. In the mean time, machine learning has been successfully demonstrated efficient handling big data. In this paper, applying machine learning to assist channel modeling and channel estimation has been introduced with evidence of literature survey.read more
Citations
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Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
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6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities
Harsh Tataria,Mansoor Shafi,Andreas F. Molisch,Mischa Dohler,Henrik Sjöland,Fredrik Tufvesson +5 more
TL;DR: This work rigorously discusses the fundamental changes required in the core networks of the future, such as the redesign or significant reduction of the transport architecture that serves as a major source of latency for time-sensitive applications.
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Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges
TL;DR: In this paper, the authors provide a comprehensive survey of recent advances and future challenges that result from bringing AI/ML technologies into B5G wireless networks, including channel measurements, modeling, and estimation, physical layer research, and network management and optimization.
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Coverage Enhancement for NLOS mmWave Links Using Passive Reflectors
TL;DR: In this paper, the use of passive metallic reflectors of different shapes/sizes to improve 28 GHz mm-wave signal coverage for both indoor and outdoor NLOS scenarios was studied, and the authors provided an analytical model for the end-to-end received power in an NLOS scenario using reflectors with different shapes and sizes.
Posted Content
Artificial Intelligence Enabled Wireless Networking for 5G and Beyond: Recent Advances and Future Challenges
TL;DR: How AI and ML can be leveraged for the design and operation of B5G networks is studied, including channel measurements, modeling, and estimation, physical layer research, and network management and optimization.
References
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Journal ArticleDOI
Machine learning
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Book
Fundamentals of Wireless Communication
David Tse,Pramod Viswanath +1 more
TL;DR: In this paper, the authors propose a multiuser communication architecture for point-to-point wireless networks with additive Gaussian noise detection and estimation in the context of MIMO networks.
Book
Foundations of Machine Learning
TL;DR: This graduate-level textbook introduces fundamental concepts and methods in machine learning, and provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application.
Journal ArticleDOI
Millimeter-Wave Cellular Wireless Networks: Potentials and Challenges
TL;DR: Measurements and capacity studies are surveyed to assess mmW technology with a focus on small cell deployments in urban environments and it is shown that mmW systems can offer more than an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks at current cell densities.
Journal ArticleDOI
An Introduction to Deep Learning for the Physical Layer
Timothy J. O'Shea,Jakob Hoydis +1 more
TL;DR: In this article, an end-to-end reconstruction task was proposed to jointly optimize transmitter and receiver components in a single process, which can be extended to networks of multiple transmitters and receivers.
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