Journal Article10.1109/MVT.2019.2921627
Model-Aided Wireless Artificial Intelligence: Embedding Expert Knowledge in Deep Neural Networks for Wireless System Optimization
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TL;DR: In this paper, the authors describe two methods that implement this strategy to optimize wireless communication networks and provide numerical results to assess the performance of the proposed approaches compared with purely data-driven implementations.
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Abstract: Deep learning based on artificial neural networks (ANNs) is a powerful machine-learning method that, in recent years, has been successfully used to realize tasks such as image classification, speech recognition, and language translation, among others, that are usually simple for human beings but extremely difficult for machines. This is one reason deep learning is considered one of the main enablers for realizing artificial intelligence (AI). The current methodology in deep learning consists of employing a data-driven approach to identify the best architecture of an ANN that allows input-output data pairs to be fitted. Once the ANN is trained, it is capable of responding to never-observed inputs by providing the optimum output based on past acquired knowledge. In this context, a recent trend in the deep-learning community complements purely data-driven approaches with prior information based on expert knowledge. In this article, we describe two methods that implement this strategy to optimize wireless communication networks. In addition, we provide numerical results to assess the performance of the proposed approaches compared with purely data-driven implementations.
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Citations
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead
Marco Di Renzo,Alessio Zappone,Merouane Debbah,Mohamed-Slim Alouini,Chau Yuen,Julien de Rosny,Sergei A. Tretyakov +6 more
TL;DR: Reconfigurable intelligent surfaces (RISs) can be realized in different ways, which include (i) large arrays of inexpensive antennas that are usually spaced half of the wavelength apart; and (ii) metamaterial-based planar or conformal large surfaces whose scattering elements have sizes and inter-distances much smaller than the wavelength.
•Posted Content
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead.
Marco Di Renzo,Alessio Zappone,Merouane Debbah,Mohamed-Slim Alouini,Chau Yuen,Julien de Rosny,Sergei A. Tretyakov +6 more
TL;DR: The emerging research field of RIS-empowered SREs is introduced; the most suitable applications of RISs in wireless networks are overviewed; an electromagnetic-based communication-theoretic framework for analyzing and optimizing metamaterial-based RISs is presented; and the most important research issues to tackle are discussed.
Toward Smart Wireless Communications via Intelligent Reflecting Surfaces: A Contemporary Survey
TL;DR: A literature review on recent applications and design aspects of the intelligent reflecting surface (IRS) in the future wireless networks, and the joint optimization of the IRS’s phase control and the transceivers’ transmission control in different network design problems, e.g., rate maximization and power minimization problems.
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Reconfigurable Intelligent Surfaces: Principles and Opportunities
TL;DR: A comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies.
1K
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Reconfigurable Intelligent Surfaces: Principles and Opportunities
Yuanwei Liu,Xiao Liu,Xidong Mu,Tianwei Hou,Jiaqi Xu,Zhijin Qin,Marco Di Renzo,Naofal Al-Dhahir +7 more
TL;DR: A comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies is provided in this article.
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