Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

Elbir, Ahmet M.; Papazafeiropoulos, Anastasios; Kourtessis, Pandelis; Chatzinotas, Symeon

doi:10.1109/lwc.2020.2993699

Cited by 234 publications

(190 citation statements)

References 16 publications

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“…In [274], the authors investigate the use of deep learning for channel estimation in a RIS-assisted massive MIMO system. In the proposed scheme, each user has an identical convolutional neural network which takes as input the received pilot signals and yields as output an estimate of the direct channel between the transmitter and receiver, and the cascaded channel from the transmitter to the receiver through the RIS.…”

Section: T Machine Learning Based Designmentioning

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.

2,045

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: T Machine Learning Based Designmentioning

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.

2,045

918

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“…Tataria et al [ 5 ] discuss practical aspects of real-time implementation of LIS, especially in terms of processing and applications in radio frequency (RF) communications. Elbir et al [ 6 ] present a deep learning framework for channel estimation, considering the massive MIMO scenario using mm-Wave.…”

Section: Introductionmentioning

confidence: 99%

Large Intelligent Surfaces Communicating Through Massive MIMO Rayleigh Fading Channels

Ferreira

Facina

Figueiredo

et al. 2020

Sensors

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Large intelligent surfaces (LIS) promises not only to improve the signal to noise ratio, and spectral efficiency but also to reduce the energy consumption during the transmission. We consider a base station equipped with an antenna array using the maximum ratio transmission (MRT), and a large reflector array sending signals to a single user. Each subchannel is affected by the Rayleigh flat fading, and the reflecting elements perform non-perfect phase correction which introduces a Von Mises distributed phase error. Based on the central limit theorem (CLT), we conclude that the overall channel has an equivalent Gamma fading whose parameters are derived from the moments of the channel fading between the antenna array and LIS, and also from the LIS to the single user. Assuming that the equivalent channel can be modeled as a Gamma distribution, we propose very accurate closed-form expressions for the bit error probability and a very tight upper bound. For the case where the LIS is not able to perform perfect phase cancellation, that is, under phase errors, it is possible to analyze the system performance considering the analytical approximations and the simulated results obtained using the well known Monte Carlo method. The analytical expressions for the parameters of the Gamma distribution are very difficult to be obtained due to the complexity of the nonlinear transformations of random variables with non-zero mean and correlated terms. Even with perfect phase cancellation, all the fading coefficients are complex due to the link between the user and the base station that is not neglected in this paper.

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“…Hierarchical beamforming codebook as well as cooperative search strategies were proposed in [18]. A deep learning framework containing a twin convolutional neural network was applied in [19] to jointly estimate the direct and cascaded channels. Tensor signal processing methods were also leveraged in [20], [21] to solve the bilinear channel recovery problem.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Based Algebraic Channel Estimation for Hybrid IRS-Assisted MIMO-OFDM

Lin

Jin

Matthaiou

et al. 2021

IEEE Trans. Wireless Commun.

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We consider the channel estimation problem in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems assisted by intelligent reconfigurable surfaces (IRSs). To avoid the inherent estimation ambiguities of the two-hop channels from mobile stations (MS) to the base station (BS), we adopt a hybrid IRS architecture composed of passive reflectors and active sensors, and establish two independent subproblems of estimating the MS-to-IRS and BS-to-IRS channels. By leveraging the sparse characteristics of high-frequency propagation, we model the training signals as multi-dimensional canonical polyadic decomposition (CPD) tensors with missing fibers or slices. We develop algebraic algorithms to solve the tensor completion problems and recover channel multipath parameters, i.e., angles of arrival, time delays and path gains. Our methods require neither random initialization nor iterative operations, and for these reasons they can perform robustly with a low computational complexity. Moreover, we investigate the uniqueness condition of CPD tensor completion, which can be utilized to inform both the physical design of hybrid IRSs and the time-frequency resource allocation of training strategies. Simulation results indicate that the proposed schemes outperform the traditional counterparts in terms of accuracy, robustness and complexity, especially for the case of low-complexity IRSs with limited number of active sensing elements.

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Deep Channel Learning for Large Intelligent Surfaces Aided mm-Wave Massive MIMO Systems

Cited by 234 publications

References 16 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

Large Intelligent Surfaces Communicating Through Massive MIMO Rayleigh Fading Channels

Tensor-Based Algebraic Channel Estimation for Hybrid IRS-Assisted MIMO-OFDM

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