Intelligent Reflecting Surface-Assisted Cognitive Radio System

Yuan, Jie; Liang, Ying-Chang; Joung, Jingon; Feng, Gang; Larsson, Erik G.

doi:10.1109/tcomm.2020.3033006

Cited by 234 publications

(94 citation statements)

References 56 publications

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“…Specifically, under the case of bound and statistical CSI error, the authors firstly investigate the robust beamforming design for primary user-related channels, and then the optimization problem of jointly phase shift matrix at the IRS and transmit precoding matrix at the secondary user is proposed to minimize the transmit power for the IRS assisted cognitive radio system when the QoS requirement of secondary user is satisfied [165]. In addition, considering the worst-case CSI error, the authors model this situation as a maximization problem in the ellipsoid region, which can be tackled by using alternating optimization and semidefinite relaxation [166][167][168]. When it comes to the robustness design in the IRS assisted THz communications, there are few studies related to this field.…”

Section: Robustness Designmentioning

confidence: 99%

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

et al. 2021

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Terahertz (THz) communications have been widely envisioned as a promising enabler to provide adequate bandwidth and achieve ultra-high data rates for sixth generation (6G) wireless networks. In order to mitigate blockage vulnerability caused by serious propagation attenuation and poor diffraction of THz waves, an intelligent reflecting surface (IRS), which manipulates the propagation of incident electromagnetic waves in a programmable manner by adjusting the phase shifts of passive reflecting elements, is proposed to create smart radio environments, improve spectrum efficiency and enhance coverage capability. Firstly, some prospective application scenarios driven by the IRS empowered THz communications are introduced, including wireless mobile communications, secure communications, unmanned aerial vehicle (UAV) scenario, mobile edge computing (MEC) scenario and THz localization scenario. Then, we discuss the enabling technologies employed by the IRS empowered THz system, involving hardware design, channel estimation, capacity optimization, beam control, resource allocation and robustness design. Moreover, the arising challenges and open problems encountered in the future IRS empowered THz communications are also highlighted. Concretely, these emerging problems possibly originate from channel modeling, new material exploration, experimental IRS testbeds and intensive deployment. Ultimately, the combination of THz communications and IRS is capable of accelerating the

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Section: Robustness Designmentioning

confidence: 99%

Towards intelligent reflecting surface empowered 6G terahertz communications: A survey

et al. 2021

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“…[82] Security in energy harvesting networks. [39] PLS in LiFi-enabled networks [105] IRS-aided covert communications RIS with emerging 6G technologies [33], [178] Discuss the coverage of a RIS-assisted largescale mmWave cellular network using stochastic geometry. [38] Compare RIS with massive MIMO, MIMO 2.0 [39] Combines RIS with light fidelity (LiFi) networks RIS-aided Localization and mapping [67] Integration of RIS with mmWave to enable localization.…”

Section: Industrial Viewpoints On Rismentioning

confidence: 99%

“…In [178], [179], the authors proposed robust CR beamforming as a solution to tackle the CSI uncertainty. CR is an effective solution to improve spectrum utilization by allowing the unlicensed SUs to share the spectrum with licensed PUs [178].…”

Section: B Ris-aided Cognitive Radio Systemsmentioning

confidence: 99%

Reconfigurable Intelligent Surface Aided Multi-User Communications: State-of-the-Art Techniques and Open Issues

Munochiveyi

Pogaku

Do³

et al. 2021

IEEE Access

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Reconfigurable intelligent surface (RIS)-aided communication is considered as an exciting research topic in academic and industrial communities since it provides an emerging affordable solution to achieve high quality and secure next-generation wireless systems. Especially, the deployment of RIS in multi-user wireless networks promises to reduce system hardware costs, signal processing complexity, as well as energy consumption due to small size, lightweight and ability to actively shape the wireless propagation environment. Further, by realizing a cost-effective radio environment, RIS-aided communication can be implemented to be an appealing technology for future integration with other emerging wireless applications and communication systems. Despite the positive appeal, RISs face new challenges that hinder integrating efficiently into wireless networks, such as network secrecy performance and system sum-rates, as well as achieving efficient deployment design in highly dynamic and time-varying wireless environments. To this end, we overview recent state-of-the-art techniques to address the above issues faced in the integration of RISs with various emerging multi-user communication techniques, such as Unmanned Aerial Vehicles (UAVs), Non-Orthogonal Multiple Access (NOMA), Millimeter Wave (mmWave) and Terahertz (THz) communications, Physical Layer Security (PLS), massive antennas, and Simultaneous Wireless Information and Power Transfer (SWIPT). Finally, we highlight promising future research directions of RIS-aided communication in Cell-Free Massive Multiple-Input-Multiple-Output (MIMO) systems, Rate-Splitting Multiple Access (RSMA), Light Fidelity (LiFi), and Cognitive Radio (CR) systems.

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“…to work with other emerging techniques such as deep learning [17], cognitive radio (CR) network [18], wireless powered communication network (WPCN) [19], full-duplex (FD) communication [20], non-orthogonal multiple access (NOMA) [21], and directional modulation (DM) [22], respectively. Summarizing the literature, the manifold optimization [10], the majorizationminimization (MM) method [13], and the semi-definite programming (SDP) with Gaussian randomization (GR) [18] are commonly used to optimize the RC.…”

Section: Introductionmentioning

confidence: 99%