Spectral and Energy Efficiency of IRS-Assisted MISO Communication With Hardware Impairments

Zhou, Shaoqing; Xu, Wei; Wang, Kezhi; Renzo, Marco Di; Alouini, Mohamed‐Slim

doi:10.1109/lwc.2020.2990431

“…6G is expected to support smart cities, the Internet of Everything (IoE) [ 39 ], tactile devices [ 9 ], and more. The requirements are high reliability [ 7 ], the high data rate in the order of 1 Tb/s, ultra-low latency of less than 1ms, high energy and spectral efficiency [ 87 ], security and privacy [ 88 ], and ubiquitous connectivity that connects everyone, including people in rural areas [ 89 , 90 ]. The Ultra-Reliable Low Latency Communication (URLLC) required in 6G networks is also under more stringent conditions than that obtainable in 5G, with delay jitters, context awareness, and UAV/Satellite compatibility being considered.…”

Section: Vision Of 6g Wireless Communicationmentioning

confidence: 99%

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Imoize

¹

,

Adedeji

²

,

Tandiya

³

et al. 2021

Sensors

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The 5G wireless communication network is currently faced with the challenge of limited data speed exacerbated by the proliferation of billions of data-intensive applications. To address this problem, researchers are developing cutting-edge technologies for the envisioned 6G wireless communication standards to satisfy the escalating wireless services demands. Though some of the candidate technologies in the 5G standards will apply to 6G wireless networks, key disruptive technologies that will guarantee the desired quality of physical experience to achieve ubiquitous wireless connectivity are expected in 6G. This article first provides a foundational background on the evolution of different wireless communication standards to have a proper insight into the vision and requirements of 6G. Second, we provide a panoramic view of the enabling technologies proposed to facilitate 6G and introduce emerging 6G applications such as multi-sensory–extended reality, digital replica, and more. Next, the technology-driven challenges, social, psychological, health and commercialization issues posed to actualizing 6G, and the probable solutions to tackle these challenges are discussed extensively. Additionally, we present new use cases of the 6G technology in agriculture, education, media and entertainment, logistics and transportation, and tourism. Furthermore, we discuss the multi-faceted communication capabilities of 6G that will contribute significantly to global sustainability and how 6G will bring about a dramatic change in the business arena. Finally, we highlight the research trends, open research issues, and key take-away lessons for future research exploration in 6G wireless communication.

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“…Based on the designed CDRN, we apply it to the developed DReL framework and propose a CDRN-based channel estimation algorithm, as shown in Fig. 6 Based on the LS-based training set defined in (16), we can obtain the following training set:…”

Section: B Cdrn-based Channel Estimation Algorithmmentioning

confidence: 99%

Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

Liu¹,

Liu²,

Ng³

et al. 2021

Preprint

0

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<div>Channel estimation is one of the main tasks in realizing practical intelligent reflecting surfaceassisted 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.</div>

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“…Based on this, an IRS can be adopted in communication systems to enhance the energy efficiency or the spectral efficiency of communication networks through passive beamforming techniques, i.e., designing an efficient configuration of phase shifts to improve the received signal-to-noise ratio (SNR) at the desired receivers [12]. Therefore, IRS-assisted communication systems and the related studies such as the network capacity or received SNR maximization [13], [14], the energy efficiency or spectral efficiency maximization [15], [16], and the IRS-assisted physical layer security [17]- [19], etc., have drawn vast attention from both the academia and the industry.…”

Section: Introductionmentioning

confidence: 99%

Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

Liu¹,

Liu²,

Ng³

et al. 2021

Preprint

View full text Add to dashboard Cite

<div>Channel estimation is one of the main tasks in realizing practical intelligent reflecting surfaceassisted 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.</div>

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Spectral and Energy Efficiency of IRS-Assisted MISO Communication With Hardware Impairments

Cited by 173 publications

References 14 publications

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

6G Enabled Smart Infrastructure for Sustainable Society: Opportunities, Challenges, and Research Roadmap

Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

Deep Residual Learning for Channel Estimation in Intelligent Reflecting Surface-Assisted Multi-User Communications

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