Intelligent Reflecting Surface Assisted Wireless Powered Communication Networks

Lyu, Bin; Hoang, Dinh Thai; Gong, Shimin; Yang, Zhen

doi:10.1109/wcncw48565.2020.9124775

Cited by 33 publications

(27 citation statements)

References 16 publications

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“…To tackle the limitation in the RCO method, most of the literature applies alternating optimization (AO) method to solve the formulated non-convex problem, where the active and passive beamformers are alternatively optimized in two independent subproblems and each subproblem can be solved efficiently. Specifically, the AO method has been widely used to solve the spectrum efficiency or energy efficiency maximization for a multi-user MISO system [82,83], non-orthogonal multiple access (NOMA) system [84,85], cognitive radio (CR) system [86], physical layer security [62,[87][88][89][90][91][92], active RIS-aided networks [93], wireless powered communication networks [94], and simultaneous wireless information and power transfer (SWIPT) systems [95]. Despite being easy to implement, the AO approach updates a block of variables alternatively in each iteration, which usually requires a large number of iterations to guarantee convergence, especially for high-dimension optimization variables.…”

Section: Alternating Optimization Methodsmentioning

confidence: 99%

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

Liang

Chen

Long

et al. 2021

Sci. China Inf. Sci.

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Reconfigurable intelligent surface (RIS), one of the key enablers for the sixth-generation (6G) mobile communication networks, is considered by designers to smartly reconfigure the wireless propagation environment in a controllable and programmable manner. Specifically, an RIS consists of a large number of low-cost and passive reflective elements (REs) without radio frequency chains. The system gain of RIS wireless systems can be achieved by adjusting the phase shifts and amplitudes of the REs so that the desired signals can be added constructively at the receiver. However, an RIS typically has limited signal processing capability and cannot perform active transmitting/receiving in general, which leads to new challenges in the physical layer design of RIS wireless systems. In this paper, we provide an overview of the RIS-aided wireless systems, including the reflection principle, channel estimation, and system design. In particular, two types of emerging RIS systems are considered: RIS-aided wireless communications (RAWC) and RISbased information transmission (RBIT), where the RIS plays the role of the reflector and the transmitter, respectively. We also envision the potential applications of RIS in 6G networks.

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Section: Alternating Optimization Methodsmentioning

confidence: 99%

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

Liang

Chen

Long

et al. 2021

Sci. China Inf. Sci.

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“…Research community has mainly focused on IRS-assisted communication scenarios, but IRS-assisted RF-powered systems has been gaining interest recently, e.g., refer to [97] for an analysis and optimization of an IRS-assisted WPCN, while an IRS-assisted SWIPT setup is investigated in [98]- [100]. Particularly attractive are the analysis and discussions carried out in [99] and [100], which are general enough to be extrapolated to purely WET-setups (the focus of our work) since EH devices and information decoding devices are considered as separate entities.…”

Section: Enhancements In Hardware and Mediummentioning

confidence: 99%

“…Some other strategies considered in the literature are input signal distribution optimization [48], [110], cooperation [24], [111], [112], hybrid automatic repeat request [113], [114], power control [40], [42], [48], [100] and rate allocation [97], [99], [115]. However, the heterogeneous traffic and QoS requirements of future IoT deployments demand adaptive use-case tailored solutions [20], which may be assessed by adopting AI/ML mechanisms allowing autonomous reconfiguration to network/channel/requirements variations.…”

Section: E Resource Scheduling and Optimizationmentioning

confidence: 99%

Massive Wireless Energy Transfer: Enabling Sustainable IoT Toward 6G Era

López

Alves

Souza

et al. 2021

IEEE Internet Things J.

132

108

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Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet-of-Things (IoT) devices enabled by the upcoming sixth-generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this article. Candidates enablers, such as energy beamforming (EB), distributed antenna systems (DASs), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling, and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultralow power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions toward realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail. Index Terms-Channel state information (CSI), distributed antenna systems (DASs), distributed ledger technology (DLT), energy beamforming (EB), intelligent reflective surfaces, Internet of Things (IoT), massive wireless energy transfer (WET), millimeter wave, sixth generation (6G), ultralow power. I. INTRODUCTION T HE SIXTH generation (6G) of wireless systems targets a data-driven sustainable society, enabled by nearinstant, secure, unlimited and green connectivity [1]-[3]. Stringent performance requirements in terms of security and

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“…There are some works on RIS-assisted WPCNs [34]- [37]. For example, the authors in [34] studied a sum-rate maximization RA problem by jointly optimizing the time scheduling and the phase-shift matrix of the RIS. But the fairness of WDs was not considered in [34].…”

Section: Introductionmentioning

confidence: 99%

“…For example, the authors in [34] studied a sum-rate maximization RA problem by jointly optimizing the time scheduling and the phase-shift matrix of the RIS. But the fairness of WDs was not considered in [34]. Considering a more practical case, the authors in [35] and [36] investigated the fairnessbased RA problem by maximizing the minimum throughput and the weighted sum rate, respectively.…”

Section: Introductionmentioning

confidence: 99%

RIS-Enhanced WPCNs: Joint Radio Resource Allocation and Passive Beamforming Optimization

Gao

Wang

et al. 2021

IEEE Trans. Veh. Technol.

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Wireless-powered communication and reconfigurable intelligent surface (RIS) can complement each other for increasing energy utilization and spectrum efficiency by reconfiguring the surrounding radio environment, however, which has not been sufficiently studied by the existing works. In this paper, we propose a joint radio resource and passive beamforming optimization scheme for a downlink RIS-assisted wireless-powered communication network with a harvest-thentransmit protocol to improve system energy efficiency (EE). In the considered model, the single-antenna wireless devices (WDs) harvest wireless energy from a multi-antenna dedicated power station (PS) through the RIS in the downlink and transmit their independent information to a single-antenna receiver in the uplink by a time-division-multiple-access mode. Our goal is to maximize the total EE of all WDs. To make full use of the beamforming gain provided by both the PS and the RIS, we jointly optimize the active beamforming of the PS and the passive beamforming of the RIS. To deal with the challenging nonconvex optimization problem with multiple coupled variables, we first consider fixing the passive beamforming, and converting the remaining radio resource allocation problem into an equivalent convex problem which is solved by using Lagrange dual theory. Then, we fix the optimized resource allocation parameters and optimize the passive beamforming of the RIS by using a semidefinite programming method. Simulation results demonstrate that the proposed algorithm achieves higher EE compared to the conventional schemes.

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Intelligent Reflecting Surface Assisted Wireless Powered Communication Networks

Cited by 33 publications

References 16 publications

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

Reconfigurable intelligent surfaces for smart wireless environments: channel estimation, system design and applications in 6G networks

Massive Wireless Energy Transfer: Enabling Sustainable IoT Toward 6G Era

RIS-Enhanced WPCNs: Joint Radio Resource Allocation and Passive Beamforming Optimization

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