Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization

Xiu, Yue; Zhao, Jun; Sun, Wei; Renzo, Marco Di; Gui, Guan; Zhang, Zhongpei; Wei, Ning

doi:10.48550/arxiv.2007.05873

“…Hence, it can mitigate the limitations resulting from narrow beams. On the other hand, instead of conventionally determining the NOMA decoding order of the users using their channel power gains, reconfiguring the RIS phase shifts allows us to flexibly design the users' decoding order [64]. Moreover, RISs can improve the data rates of weak UEs without the need for additional transmit power [65].…”

Section: B Nomamentioning

confidence: 99%

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

Chaccour

¹

,

Soorki

²

,

Saad

³

et al. 2022

IEEE Commun. Surv. Tutorials

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Wireless communication at the terahertz (THz) frequency bands (0.1 − 10 THz) is viewed as one of the cornerstones of tomorrow's 6G wireless systems. Owing to the large amount of available bandwidth, if properly deployed, THz frequencies can potentially provide significant wireless capacity performance gains and enable high-resolution environment sensing. However, operating a wireless system at high-frequency bands such as THz is limited by a highly uncertain and dynamic channel. Effectively, these channel limitations lead to unreliable intermittent links as a result of an inherently short communication range, and a high susceptibility to blockage and molecular absorption. Consequently, such impediments could disrupt the THz band's promise of highrate communications and high-resolution sensing capabilities. In this context, this paper panoramically examines the steps needed to efficiently and reliably deploy and operate next-generation THz wireless systems that will synergistically support a fellowship of communication and sensing services. For this purpose, we first set the stage by describing the fundamentals of the THz frequency band. Based on these fundamentals, we characterize and comprehensively investigate seven unique defining features of THz wireless systems: 1) Quasi-opticality of the band, 2) THztailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHYlayer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization. These seven defining features allow us to shed light on how to re-engineer wireless systems as we know them today so as to make them ready to support THz bands and their unique environments. On the one hand, THz systems benefit from their quasi-opticality and can turn every communication challenge into a sensing opportunity, thus contributing to a new generation of versatile wireless systems that can perform multiple functions beyond basic communications. On the other hand, THz systems can capitalize on the role of intelligent surfaces, lower frequency bands, and machine learning (ML) tools to guarantee a robust system performance. We conclude our exposition by presenting the key THz 6G use cases along with their associated major challenges and open problems. Ultimately, the goal of this article is to chart a forward-looking roadmap that exposes the necessary solutions and milestones for enabling THz frequencies to realize

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“…Hence, it can mitigate the limitations resulting from narrow beams. On the other hand, instead of conventionally determining the NOMA decoding order of the users using their channel power gains, reconfiguring the RIS phase shifts allows us to flexibly design the users' decoding order [62]. Moreover, RISs can improve the data rates of weak UEs without the need for additional transmit power [63].…”

Section: B Nomamentioning

confidence: 99%

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

Chaccour¹,

Soorki²,

Saad³

et al. 2021

Preprint

View full text Add to dashboard Cite

Wireless communication at the terahertz (THz) frequency bands (0.1 − 10 THz) is viewed as one of the cornerstones of tomorrow's 6G wireless systems. Owing to the large amount of available bandwidth, if properly deployed, THz frequencies can potentially provide significant wireless capacity performance gains and enable high-resolution environment sensing. However, operating a wireless system at high-frequency bands such as THz is limited by a highly uncertain and dynamic channel. Effectively, these channel limitations lead to unreliable intermittent links as a result of an inherently short communication range, and a high susceptibility to blockage and molecular absorption. Consequently, such impediments could disrupt the THz band's promise of highrate communications and high-resolution sensing capabilities. In this context, this paper panoramically examines the steps needed to efficiently and reliably deploy and operate next-generation THz wireless systems that will synergistically support a fellowship of communication and sensing services. For this purpose, we first set the stage by describing the fundamentals of the THz frequency band. Based on these fundamentals, we characterize and comprehensively investigate seven unique defining features of THz wireless systems: 1) Quasi-opticality of the band, 2) THztailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHYlayer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization. These seven defining features allow us to shed light on how to re-engineer wireless systems as we know them today so as to make them ready to support THz bands and their unique environments. On the one hand, THz systems benefit from their quasi-opticality and can turn every communication challenge into a sensing opportunity, thus contributing to a new generation of versatile wireless systems that can perform multiple functions beyond basic communications. On the other hand, THz systems can capitalize on the role of intelligent surfaces, lower frequency bands, and machine learning (ML) tools to guarantee a robust system performance. We conclude our exposition by presenting the key THz 6G use cases along with their associated major challenges and open problems. Ultimately, the goal of this article is to chart a forward-looking roadmap that exposes the necessary solutions and milestones for enabling THz frequencies to realize

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“…The works in [23]- [25] model the RIS-aided channels when the authors consider the RISs as linear materials. When considering the RISs as integrated antennas, channel models are proposed with performance analysis, such as channel models based on zero-forcing beamforming designs in [26], [27]. Under the presence of hardware impairment of RIS-aided networks, the physical layer performance analysis is investigated by deriving the outage probability and throughput expressions in [28], and the security performance of a RIS-aided internet of things (IoT) NOMA network is also analyzed in [29].…”

Section: Introductionmentioning

confidence: 99%

STAR-IOS Aided NOMA Networks: Channel Model Approximation and Performance Analysis

Zhang,

Yi,

Liu

et al. 2021

Preprint

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Compared with the conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting intelligent omini-surfaces (STAR-IOSs) are able to achieve 360 • coverage "smart radio environments". By splitting the energy or altering the active number of STAR-IOS elements, STAR-IOSs provide high flexibility of successive interference cancellation (SIC) orders for non-orthogonal multiple access (NOMA) systems. Based on the aforementioned advantages, this paper investigates a STAR-IOSaided downlink NOMA network with randomly deployed users. We first propose three tractable channel models for different application scenarios, namely the central limit model, the curve fitting model, and the M-fold convolution model. More specifically, the central limit model fits the scenarios with large-size STAR-IOSs while the curve fitting model is extended to evaluate multi-cell networks. However, these two models cannot obtain accurate diversity orders. Hence, we figure out the M-fold convolution model to derive accurate diversity orders. We consider three protocols for STAR-IOSs, namely, the energy splitting (ES) protocol, the time switching (TS) protocol, and the mode switching (MS) protocol. Based on the ES protocol, we derive closed-form analytical expressions of outage probabilities for the paired NOMA users by the central limit model and the curve fitting model. Based on three STAR-IOS protocols, we derive the diversity gains of NOMA users by the M-fold convolution model. The analytical results reveal that the diversity gain of NOMA users is equal to the active number of STAR-IOS elements.Numerical results indicate that 1) in high signal-to-noise ratio regions, the central limit model performs

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Reconfigurable Intelligent Surfaces Aided mmWave NOMA: Joint Power Allocation,Phase Shifts, and Hybrid Beamforming Optimization

Cited by 3 publications

References 44 publications

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

STAR-IOS Aided NOMA Networks: Channel Model Approximation and Performance Analysis

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