Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations

Aygül, Mehmet Ali; Nazzal, Mahmoud; Arslan, Hüseyin

doi:10.1109/wcnc49053.2021.9417591

Cited by 19 publications

(19 citation statements)

References 21 publications

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“…Configuring reflecting factors without large channel estimation or the use of beam training is significantly difficult. In [87], the authors considered RIS-aided wireless networks and proposed a phase optimization technique by taking advantage of the correlation between the previously calculated and current channels. An MLP model was developed to enhance the quality of optimum RIS communication.…”

Section: B Beamformingmentioning

confidence: 99%

Machine Learning Approaches for Reconfigurable Intelligent Surfaces: A Survey

Faisal

Choi

2022

IEEE Access

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Next-generation wireless networks must handle a growing density of mobile users while accommodating a rapid increase in mobile data traffic flow and a wide variety of services and applications. High-frequency waves will perform an essential role in future networks, but these signals are easily obstructed by objects and diminish over long distances. Reconfigurable intelligent surfaces (RISs) have attracted considerable interest because of their potential to improve wireless network capacity and coverage by intelligently changing the wireless propagation environment. Consequently, RISs possess potential technology for the sixth generation of communication networks. In addition, since machine learning (ML) is a promising strategy for improving a network and its performance, the application of ML in RISs is expected to open new avenues for interdisciplinary studies as well as practical applications. In this paper, we extensively investigate the ML algorithms used in RISs and provide a brief overview of RISs, a summary of ML methods with RIS architecture, and a comparison of the available methodologies to explain the combination of these two technologies. Moreover, the significance of open research topics is emphasized to provide sound research directions.

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Section: B Beamformingmentioning

confidence: 99%

Machine Learning Approaches for Reconfigurable Intelligent Surfaces: A Survey

Faisal

Choi

2022

IEEE Access

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“…More than a 99% accuracy was achieved by the proposed model. The OFDM-based single receiving antenna system model has been utilized in the IRS network to achieve a maximum performance rate with the SL algorithm [ 35 , 77 , 78 ]. The study in [ 77 ] proposed an ordinary differential equation (ODE)-based CNN model for IRS-based communication.…”

Section: Machine Learning For Irs-assisted Communication Systemsmentioning

confidence: 99%

“…The performance analysis shows that ODE-based CNN is always superior compared to a standard CCN network. The authors in [ 78 ] proposed an algorithm that leverages previous channel information to improve the quality of an optimal IRS interaction. The authors of [ 79 ] proposed a SL algorithm in the OFDM-based single input single output (SISO) system to obtain a maximum performance in the IRS network.…”

Section: Machine Learning For Irs-assisted Communication Systemsmentioning

confidence: 99%

Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review

Sejan

Rahman

Shin

et al. 2022

Sensors

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An intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic waves propagation by changing the electric and magnetic properties of its surface. Therefore, IRS is considered a smart technology for the sixth generation (6G) of communication networks. In addition, machine learning (ML) techniques are now widely adopted in wireless communication as the computation power of devices has increased. As it is an emerging topic, we provide a comprehensive overview of the state-of-the-art on ML, especially on deep learning (DL)-based IRS-enhanced communication. We focus on their operating principles, channel estimation (CE), and the applications of machine learning to IRS-enhanced wireless networks. In addition, we systematically survey existing designs for IRS-enhanced wireless networks. Furthermore, we identify major issues and research opportunities associated with the integration of IRS and other emerging technologies for applications to next-generation wireless communication.

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“…Therefore, the literature frequently focuses on this subject. DL-based channel estimation techniques reducing the training overhead are also presented in [226], [227], and [228] by different learning and network architectures. [227] aimed to strengthen the interaction between channel information and passive beamforming of conventional DL-based systems by practicing the multi-layer perceptron (MLP) architecture.…”

Section: A Channel Estimation and Signal Detection For Ris-assisted D...mentioning

confidence: 99%

“…DL-based channel estimation techniques reducing the training overhead are also presented in [226], [227], and [228] by different learning and network architectures. [227] aimed to strengthen the interaction between channel information and passive beamforming of conventional DL-based systems by practicing the multi-layer perceptron (MLP) architecture. [228] presented a two-stage channel estimation method to reduce the training load in mmWave communication schemes.…”

Section: A Channel Estimation and Signal Detection For Ris-assisted D...mentioning

confidence: 99%

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Ozpoyraz¹,

Dogukan²,

Gevez³

et al. 2022

Preprint

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Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

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Deep Learning-Based Optimal RIS Interaction Exploiting Previously Sampled Channel Correlations

Cited by 19 publications

References 21 publications

Machine Learning Approaches for Reconfigurable Intelligent Surfaces: A Survey

Machine Learning Approaches for Reconfigurable Intelligent Surfaces: A Survey

Machine Learning for Intelligent-Reflecting-Surface-Based Wireless Communication towards 6G: A Review

Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

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