Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications
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TL;DR: In this paper, a CNN denoising block equipped with an element-wise subtraction structure is designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously.
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Abstract: Channel estimation is one of the main tasks in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MUC) systems. However, different from traditional communication systems, an IRS-MUC system generally involves a cascaded channel with a sophisticated statistical distribution. In this case, the optimal minimum mean square error (MMSE) estimator requires the calculation of a multidimensional integration which is intractable to be implemented in practice. To further improve the channel estimation performance, in this paper, we model the channel estimation as a denoising problem and adopt a deep residual learning (DReL) approach to implicitly learn the residual noise for recovering the channel coefficients from the noisy pilot-based observations. To this end, we first develop a versatile DReL-based channel estimation framework where a deep residual network (DRN)-based MMSE estimator is derived in terms of Bayesian philosophy. As a realization of the developed DReL framework, a convolutional neural network (CNN)-based DRN (CDRN) is then proposed for channel estimation in IRS-MUC systems, in which a CNN denoising block equipped with an element-wise subtraction structure is specifically designed to exploit both the spatial features of the noisy channel matrices and the additive nature of the noise simultaneously. In particular, an explicit expression of the proposed CDRN is derived and analyzed in terms of Bayesian estimation to characterize its properties theoretically. Finally, simulation results demonstrate that the performance of the proposed method approaches that of the optimal MMSE estimator requiring the availability of the prior probability density function of channel.
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Citations
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Robust Max-Min Fairness Transmission Design for IRS-Aided Wireless Network Considering User Location Uncertainty
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IRS-Assisted Secure UAV Communication System for Multiuser With Hardware Impairments
TL;DR: In this article , a secure multiuser communication of an intelligent reflecting surface (IRS)-aided unmanned aerial vehicle (UAV) system with HIs at the transceiver and the IRS is studied.
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Multi-Task Learning-Based Channel Estimation for RIS Assisted Multi-User Communication Systems
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TL;DR: In this article , a multi-task learning (MTL)-based joint channel estimation scheme is proposed for reconfigurable intelligent surface (RIS) assisted millimeter-wave communication system, where the direct channel (DC) and cascaded channel (CC) are estimated at the same coherence time by learning the feature of shared pilots.
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