Intelligent Reflecting Surface: Practical Phase Shift Model and Beamforming Optimization

By Samith Abeywickrama, Rui Zhang, Chau Yuen. ICC 2020 - 2020 IEEE International Conference on Communications (ICC), 2019

663 citations recorded by the source corpus.

Intelligent reflecting surface (IRS) that enables the control of the wireless propagation environment has been looked upon as a promising technology for boosting the spectrum and energy efficiency in future wireless communication systems. Prior works on IRS are mainly based on the ideal phase shift model assuming the full signal reflection by each of the elements regardless of its phase shift, which, however, is practically difficult to realize. In contrast, we propose in this paper a practical phase shift model that captures the phase-dependent amplitude variation in the element-wise reflection coefficient. Applying this new model to an IRS-aided wireless system, we formulate a problem to maximize its achievable rate by jointly optimizing the transmit beamforming and the IRS reflect beamforming. The formulated problem is non-convex and difficult to be optimally solved in general, for which we propose a low-complexity suboptimal solution based on the alternating optimization (AO) technique. Simulation results unveil a substantial performance gain achieved by the joint beamforming optimization based on the proposed phase shift model as compared to the conventional ideal model.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c598bfe5b6a1e2c4ae44d416f631b07c76e8087d

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Citations in the corpus

Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing (2019, background): “2) Intelligent Reflecting Surface Aided Wireless Networks: In order to explore the benefits of IRSs in wireless communications, extensive research efforts have been invested into their ergodic capacity analysis [19], channel estimation [20], and practical reflection phase shift modeling [21], as wel”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How it Works, State of Research, and Road Ahead (2020, background): “In [145], the authors develop a phase shift model that relates the RIS phase shifts to the amplitude of the reflection coefficient.”
MIMO Transmission Through Reconfigurable Intelligent Surface: System Design, Analysis, and Implementation (2019, background): “During the design process of the unit cell structure, researchers tend to make the fluctuation of the amplitude response as small as possible, while obtaining a sufficiently large range for phase response control [39]–[41].”
Power Scaling Laws and Near-Field Behaviors of Massive MIMO and Intelligent Reflecting Surfaces (2020, background): “Depending on the IRS implementation, the phases and amplitudes might not be independently controllable but coupled [47].”
Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer (2019, methodology): “A sophisticated phase shift model was derived in [11], by taking into account a realistic amplitude-phase relationship.”
Wireless Communications With Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement (2019, background): “The impact of hardware imperfections, like discrete phase-shift and phase-dependent amplitude, was studied in [29], [33], [34].”
Reconfigurable Intelligent Surfaces: A signal processing perspective with wireless applications (2021, neutral): “The upper part of Figure 1(b) shows the frequency response for the element implementation considered in [20].”
Active Reconfigurable Intelligent Surface-Aided Wireless Communications (2021, background): “This dependency can be attenuated with the improvement over the effective resistance of the load impedance generating circuit [35].”
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking (2021, background): “To tackle this issue, IRS/RIS, which is able to reconfigure the propagation environment by providing adjustable reflected paths for signals [81]– [83], has attracted increasing attention.”
Reconfigurable Intelligent Surfaces for Wireless Communications: Principles, Challenges, and Opportunities (2020, background): “Recent works have shown the potential of RISs in pointto-point channels [7], [13], [31]–[42], downlink broadcast channels [43]–[52], uplink multiple access channels [53]–[56],”
An Overview of Signal Processing Techniques for RIS/IRS-Aided Wireless Systems (2021, neutral): “It is worth noting that some works considered practical RIS reﬂection models with phase-dependent amplitude [75]–[77] or reﬂection models that account for the mutual coupling among the reﬂecting elements [78]–[80].”
Reconfigurable Intelligent Surface-Based Index Modulation: A New Beyond MIMO Paradigm for 6G (2019, neutral): “It is worth noting that more practical channel modeling and phase shift design [27] along with effective channel estimation techniques [28] are open research topics with the context of RISs and the considered model may shed light on performance limits of future schemes.”
Reconfigurable Intelligent Surfaces: Principles and Opportunities (2020, background): “Moreover, according to [36], βi and φi in (1) are correlated and related through a compact function: βi = f(φi).”
Physics-Based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces (2020, methodology): “[13], [16], [17], [22], [32] have a complexity order that is at least cubic in Q, i.”
Intelligent Reflecting Surface Aided Multigroup Multicast MISO Communication Systems (2019, background): “Please note that the design of the practical reflection amplitude which was modeled as a function of the phase shifts [21] is more complex and will be investigated in our future work.”
Smart Radio Environments Empowered by Reconfigurable Intelligent Surfaces: How It Works, State of Research, and The Road Ahead (2020, background): “In [146], the authors develop a phase shift model that relates the RIS phase shifts to the amplitude of the reflection coefficient.”
Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band (2021, background): “This is 475 because, in general, the amplitude and the phase of Γn,m are 476 not independent of each other [35], [36].”