A Deep Learning Based Modeling of Reconfigurable Intelligent Surface Assisted Wireless Communications for Phase Shift Configuration

Sheen, Baoling; Yang, Jin; Feng, Xianglong; Chowdhury, Moin Uddin

doi:10.1109/ojcoms.2021.3050119

Cited by 65 publications

(43 citation statements)

References 24 publications

(53 reference statements)

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“…However, channel acquisition is a significant challenge in RIS-assisted schemes due to complicated channel information caused by a large number of reflective elements. Therefore, [245] and [218] proposed approaches unveiling a direct mapping between optimal phase configuration and achievable rate. [218] uses the pilot signals in the DNN trained by unsupervised learning approach to determine the optimal phase configuration instead of using it in the channel acquisition.…”

Section: Referencementioning

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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Section: Referencementioning

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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“…Then, the dataset was used to train a properly designed DNN for learning the mapping from user location to the optimal IRS passive beamforming. This method was further extended in [163] to predict the achievable rate at any user location. However, the performance of IRS passive beamforming in practice is not merely determined by user location, but also other parameters such as the small-scale fading that cannot be fully characterized by the locations of the transmitter and receiver.…”

Section: ) Beam Training and Channel Trackingmentioning

confidence: 99%

A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications

Zheng¹,

You²,

Mei³

et al. 2021

Preprint

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Intelligent reflecting surface (IRS) has emerged as a key enabling technology to realize smart and reconfigurable radio environment for wireless communications, by digitally controlling the signal reflection via a large number of passive reflecting elements in real time. Different from conventional wireless communication techniques that only adapt to but have no or limited control over dynamic wireless channels, IRS provides a new and cost-effective means to combat the wireless channel impairments in a proactive manner. However, despite its great potential, IRS faces new and unique challenges in its efficient integration into wireless communication systems, especially its channel estimation and passive beamforming design under various practical hardware constraints. In this paper, we provide a comprehensive survey on the up-to-date research in IRS-aided wireless communications, with an emphasis on the promising solutions to tackle practical design issues. Furthermore, we discuss new and emerging IRS architectures and applications as well as their practical design problems to motivate future research.

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“…Due to the constraints defined over environments, (7) boils down to a bilevel optimization problem that requires lowerlevel optimizations per environment, which makes solving (7) challenging. Alternatively, following the IRM framework [10], we recast (7b) as a penalized loss as follows:…”

Section: Irm-based Phase Optimizationmentioning

confidence: 99%

Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations

Samarakoon¹,

Park²,

Bennis³

2021

Preprint

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In this paper, the problem of robust reconfigurable intelligent surface (RIS) system design under changes in data distributions is investigated. Using the notion of invariant risk minimization (IRM), an invariant causal representation across multiple environments is used such that the predictor is simultaneously optimal for each environment. A neural network-based solution is adopted to seek the predictor and its performance is validated via simulations against an empirical risk minimizationbased design. Results show that leveraging invariance yields more robustness against unseen and out-of-distribution testing environments.

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A Deep Learning Based Modeling of Reconfigurable Intelligent Surface Assisted Wireless Communications for Phase Shift Configuration

Cited by 65 publications

References 24 publications

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

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

A Survey on Channel Estimation and Practical Passive Beamforming Design for Intelligent Reflecting Surface Aided Wireless Communications

Robust Reconfigurable Intelligent Surfaces via Invariant Risk and Causal Representations

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