Open AccessPosted Content
Enabling Large Intelligent Surfaces with Compressive Sensing and Deep Learning
TLDR
The achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.Abstract:
Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise a massive number of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full knowledge of the channels. Estimating these channels at the LIS, however, is a key challenging problem, and is associated with large training overhead given the massive number of LIS elements. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband of the LIS controller). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. These full channels can then be used to design the LIS reflection matrices with no training overhead. In the second approach, we develop a deep learning based solution where the LIS learns how to optimally interact with the incident signal given the channels at the active elements, which represent the current state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed compressive sensing and deep learning solutions approach the upper bound, that assumes perfect channel knowledge, with negligible training overhead and with less than 1% of the elements being active.read more
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Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network
Qingqing Wu,Rui Zhang +1 more
TL;DR: This article addresses the key challenges in designing and implementing the new IRS-aided hybrid (with both active and passive components) wireless network, as compared to the traditional network comprising active components only.
Journal ArticleDOI
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.
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Holographic MIMO Surfaces for 6G Wireless Networks: Opportunities, Challenges, and Trends
Chongwen Huang,Sha Hu,George C. Alexandropoulos,Alessio Zappone,Chau Yuen,Rui Zhang,Marco Di Renzo,Merouane Debbah +7 more
TL;DR: An overview of HMIMOS communications including the available hardware architectures for reconfiguring such surfaces are provided, and the opportunities and key challenges in designingHMIMOS-enabled wireless communications are highlighted.
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Multicell MIMO Communications Relying on Intelligent Reflecting Surfaces
TL;DR: This paper proposes to invoke an IRS at the cell boundary of multiple cells to assist the downlink transmission to cell-edge users, whilst mitigating the inter-cell interference, which is a crucial issue in multicell communication systems.
Journal ArticleDOI
Wireless Communications with Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement
Wankai Tang,Ming Zheng Chen,Xiangyu Chen,Jun Yan Dai,Yu Han,Marco Di Renzo,Yong Zeng,Shi Jin,Qiang Cheng,Tie Jun Cui +9 more
TL;DR: In this article, free-space path loss models for RIS-assisted wireless communications are developed for different scenarios by studying the physics and electromagnetic nature of RISs, which reveal the relationships between the free space path loss of RIS assisted wireless communications and the distance from the transmitter/receiver to the RIS, the size of the RIS and the radiation patterns of antennas and unit cells.
References
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Multilayer feedforward networks are universal approximators
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Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
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Greed is good: algorithmic results for sparse approximation
TL;DR: This article presents new results on using a greedy algorithm, orthogonal matching pursuit (OMP), to solve the sparse approximation problem over redundant dictionaries and develops a sufficient condition under which OMP can identify atoms from an optimal approximation of a nonsparse signal.
Journal ArticleDOI
Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming
Qingqing Wu,Rui Zhang +1 more
TL;DR: Simulation results demonstrate that an IRS-aided single-cell wireless system can achieve the same rate performance as a benchmark massive MIMO system without using IRS, but with significantly reduced active antennas/RF chains.
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