Passive Beamforming and Information Transfer via Large Intelligent Surface

Yan, Wen-Jing; Yuan, Xiaojun; Kuai, Xiaoyan

doi:10.1109/lwc.2019.2961670

Cited by 207 publications

(135 citation statements)

References 23 publications

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“…In [84], the authors apply the principle of spatial modulation by modulating information onto the ON/OFF states of the reflecting elements of RISs. In addition, passive beamforming is obtained by adjusting the phase shifts of the activated reflecting elements.…”

Section: F Surface-based Modulation and Encodingmentioning

confidence: 99%

Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead

Renzo

Zappone

Debbah

et al. 2020

IEEE J. Select. Areas Commun.

2,226

918

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What is a reconfigurable intelligent surface? What is a smart radio environment? What is a metasurface? How do metasurfaces work and how to model them? How to reconcile the mathematical theories of communication and electromagnetism? What are the most suitable uses and applications of reconfigurable intelligent surfaces in wireless networks? What are the most promising smart radio environments for wireless applications? What is the current state of research? What are the most important and challenging research issues to tackle? These are a few of the many questions that we investigate in this short opus, which has the threefold objective of introducing the emerging research field of smart radio environments empowered by reconfigurable intelligent surfaces, putting forth the need of reconciling and reuniting C. E. Shannon's mathematical theory of communication with G. Green's and J. C. Maxwell's mathematical theories of electromagnetism, and reporting pragmatic guidelines and recipes for employing appropriate physics-based models of metasurfaces in wireless communications.

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Section: F Surface-based Modulation and Encodingmentioning

confidence: 99%

Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead

Renzo

Zappone

Debbah

et al. 2020

IEEE J. Select. Areas Commun.

2,226

918

View full text Add to dashboard Cite

show abstract

“…The BiG-AMP algorithm is a computationally efficient iterative procedure by leveraging the approximate message passing to solve the MAP inference problem (6). Details of the BiG-AMP can be found in Lines 1 to 22 of Algorithm 1.…”

Section: A Sparse Matrix Factorizationmentioning

confidence: 99%

Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO

Yuan

2020

IEEE Wireless Commun. Lett.

Self Cite

719

465

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In this paper, we consider the problem of channel estimation for large intelligent metasurface (LIM) assisted massive multiple-input multiple-output (MIMO) systems. The main challenge of this problem is that the LIM with a large number of low-cost metamaterial antennas can only passively reflect the incident signal by a certain phase shift, and does not have any signal processing capability. We introduce a general framework for the estimation of the transmitter-LIM and LIM-receiver cascaded channel, and propose a three-stage algorithm that includes a sparse matrix factorization stage, a probabilistic ambiguity elimination stage, and a matrix completion stage. Simulation results illustrate that the proposed algorithm can achieve accurate channel estimation for LIM-assisted massive MIMO systems.

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“…According to the obtained solutions in the previous two subproblems, the overall algorithm for solving problem (9) is summarized in Algorithm 1, where ǫ is used to control the accuracy of convergence. Following the results in [3], we can guarantee that the average achievable rate by solving problem (9) is non-decreasing over iterations.…”

Section: Overall Algorithmmentioning

confidence: 99%

Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming

Duo

Yuan

et al. 2020

IEEE Wireless Commun. Lett.

Self Cite

584

335

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Thanks to the line-of-sight (LoS) transmission and flexibility, unmanned aerial vehicles (UAVs) effectively improve the throughput of wireless networks. Nevertheless, the LoS links are prone to severe deterioration by complex propagation environments, especially in urban areas. Reconfigurable intelligent surfaces (RISs), as a promising technique, can significantly improve channel fading effect and enhance communication quality by intelligently reflecting the received signals. Motivated by this, the joint UAV trajectory and RIS's passive beamforming design for a novel RIS-assisted UAV communication system is investigated to maximize the average achievable rate in this letter. To solve the formulated non-convex problem, we divide it into two subproblems, i.e., passive beamforming and trajectory optimization, respectively. We first derive a closed-form phaseshift solution with any given UAV trajectory to achieve the phase alignment of the received signals from different transmission paths. Then, with the given optimal phase-shift solution, we obtain the locally optimal trajectory solution by using the successive convex approximation (SCA) method. Numerical results depict that the proposed algorithm can considerably improve the average achievable rate.Index Terms-UAV communication, trajectory design, reconfigurable intelligent surface, passive beamforming.

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Passive Beamforming and Information Transfer via Large Intelligent Surface

Cited by 207 publications

References 23 publications

Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead

Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead

Cascaded Channel Estimation for Large Intelligent Metasurface Assisted Massive MIMO

Reconfigurable Intelligent Surface Assisted UAV Communication: Joint Trajectory Design and Passive Beamforming

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