Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization

Abeywickrama, Samith; Zhang, Rui; Wu, Qingqing; Yuen, Chau

doi:10.1109/tcomm.2020.3001125

Cited by 579 publications

(165 citation statements)

References 28 publications

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“…Namely, Rician 3 The maximum rate is obtained when the amplitude of each reflection coefficient is set to unity, which represents an ideal scenario. However, in practical scenarios, the reflection coefficients are usually less than 1, and can also depend on the values of the phase shifts, as demonstrated in [13]. 4 Links between S → R and R → D are assumed to experience Rayleigh fading at all times as they can be mobile users in a dense urban network for example, and therefore it is not guaranteed to have a LoS signal between them.…”

Section: Performance Analysis and Optimization Of The Proposed Hmentioning

confidence: 99%

A Hybrid Relay and Intelligent Reflecting Surface Network and Its Ergodic Performance Analysis

Abdullah

Chen

Lambotharan

et al. 2020

IEEE Wireless Commun. Lett.

138

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This letter proposes a novel hybrid relay and Intelligent Reflecting Surface (IRS) assisted system for future wireless networks. We demonstrate that for practical scenarios where the amount of radiated power and/or the number of reflecting elements are/is limited, the performance of an IRS-supported system can be significantly enhanced by utilizing a simple Decodeand-Forward (DF) relay. Tight upper bounds for the ergodic capacity are derived for the proposed scheme under different channel environments, and shown to closely match Monte-Carlo simulations.

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Section: Performance Analysis and Optimization Of The Proposed Hmentioning

confidence: 99%

A Hybrid Relay and Intelligent Reflecting Surface Network and Its Ergodic Performance Analysis

Abdullah

Chen

Lambotharan

et al. 2020

IEEE Wireless Commun. Lett.

138

View full text Add to dashboard Cite

show abstract

“…For b 2 − 4ac ≤ 0 corresponding to k w and m w being conjugate complex numbers, k w and m w are set to the estimated modulus values of the conjugate complex number. From (1) and 3, the PDF of γ 1 can be represented by a squared K G distribution. Thus, the PDF of γ 1 can be written as…”

Section: A Ris-dh Casementioning

confidence: 99%

“…Owing to their promising gains, RISs have been extensively investigated in the literature along the last year. In particular, in [1], the authors proposed a practical phase shift model for the RISs. In [2], the authors provided a comprehensive overview of the basic characteristics of the large intelligent surface/antenna (LISA) technology, and listed various potential application examples.…”

Section: Introductionmentioning

confidence: 99%

Accurate Closed-Form Approximations to Channel Distributions of RIS-Aided Wireless Systems

Yang

Meng

et al. 2020

IEEE Wireless Commun. Lett.

Self Cite

141

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This paper proposes highly accurate closed-form approximations to channel distributions of two different reconfigurable intelligent surface (RIS)-based wireless system setups, namely, dual-hop RIS-aided (RIS-DH) scheme and RIS-aided transmit (RIS-T) scheme. Differently from previous works, the proposed approximations reveal to be very tight for arbitrary number N of reflecting metasurface's elements. Our findings are then applied to the performance analysis of the considered systems, in which the outage probability, bit error rate, and average channel capacity are derived. Results show that the achievable diversity orders G d for RIS-DH and RIS-T schemes are N − 1 < G d < N and N , respectively. Furthermore, it is revealed that both schemes can not provide the multiplexing gain and only diversity gains are achieved. For the RIS-DH scheme, the channels are similar to the keyhole multiple-input multipleoutput (MIMO) channels with only one degree of freedom, while the RIS-T scheme is like the transmit diversity structure.

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“…This implementation is not practical, due to hardware limitations. In contrast, [55] introduces a feasible phase shift model with a reflection coefficient which apprehends the phase-dependent amplitude for a MISO wireless system. The IRS controller is utilized to communicate with the AP to control the IRS reflections, where the IRS reflecting elements are programmable by the controller.…”

Section: Sum-rate Maximizationmentioning

confidence: 99%

“…Based on these parameters, Fig. 6 depicts the system model that is considered in [55] to formulate the optimization problem that maximizes the achievable rate by optimizing both the transmit beamforming vector w at the AP and the reflect beamforming vector v at the IRS as follows…”

Section: Sum-rate Maximizationmentioning

confidence: 99%

Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

et al. 2020

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This paper surveys the optimization frameworks and performance analysis methods for large intelligent surfaces (LIS), which have been emerging as strong candidates to support the sixth-generation wireless physical platforms (6G). Due to their ability to adjust the behavior of interacting electromagnetic (EM) waves through intelligent manipulations of the reflections phase shifts, LIS have shown promising merits at improving the spectral efficiency of wireless networks. In this context, researchers have been recently exploring LIS technology in depth as a means to achieve programmable, virtualized, and distributed wireless network infrastructures. From a system level perspective, LIS have also been proven to be a lowcost, green, sustainable, and energy-efficient solution for 6G systems. This paper provides a unique blend that surveys the principles of operation of LIS, together with their optimization and performance analysis frameworks. The paper first introduces the LIS technology and its physical working principle. Then, it presents various optimization frameworks that aim to optimize specific objectives, namely, maximizing energy efficiency, sum-rate, secrecy-rate, and coverage. The paper afterwards discusses various relevant performance analysis works including capacity analysis, the impact of hardware impairments on capacity, uplink/downlink data rate analysis, and outage probability. The paper further presents the impact of adopting the LIS technology for positioning applications. Finally, we identify numerous exciting open challenges for LIS-aided 6G wireless networks, including resource allocation problems, hybrid radio frequency/visible light communication (RF-VLC) systems, health considerations, and localization. INDEX TERMS 6G technology, large intelligent surfaces (LIS), massive multiple-input multiple-output (mMIMO), millimetre waves (mmWave) communication, wireless communication.

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Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization

Cited by 579 publications

References 28 publications

A Hybrid Relay and Intelligent Reflecting Surface Network and Its Ergodic Performance Analysis

A Hybrid Relay and Intelligent Reflecting Surface Network and Its Ergodic Performance Analysis

Accurate Closed-Form Approximations to Channel Distributions of RIS-Aided Wireless Systems

Intelligent Surfaces for 6G Wireless Networks: A Survey of Optimization and Performance Analysis Techniques

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