Impact of Channel Models on Performance Characterization of RIS-Assisted Wireless Systems

The performance characterization of communication systems assisted by large reconfigurable intelligent surfaces (RISs) significantly depends on the adopted models for the underlying channels. Under unrealistic channel models, the system performance may be over- or under-estimated which yields inaccurate conclusions for the system design. In this paper, we review five channel models that are chosen to progressively improve the modeling accuracy for large RISs. For each channel model, we highlight the underlying assumptions, its advantages, and its limitations. We compare the system performance under the aforementioned channel models using RIS configuration algorithms from the literature and a new scalable algorithm proposed in this paper specifically for the configuration of extremely large RISs.

[1]  Emil Björnson,et al.  Near-Field Beamforming and Multiplexing Using Extremely Large Aperture Arrays , 2022, ArXiv.

[2]  H. Poor,et al.  Near-Field Hierarchical Beam Management for RIS-Enabled Millimeter Wave Multi-Antenna Systems , 2022, 2022 IEEE 12th Sensor Array and Multichannel Signal Processing Workshop (SAM).

[3]  H. Vincent Poor,et al.  Low-to-Zero-Overhead IRS Reconfiguration: Decoupling Illumination and Channel Estimation , 2021, IEEE Communications Letters.

[4]  Derrick Wing Kwan Ng,et al.  Smart and Reconfigurable Wireless Communications: From IRS Modeling to Algorithm Design , 2021, IEEE Wireless Communications.

[5]  Emil Björnson,et al.  Rayleigh Fading Modeling and Channel Hardening for Reconfigurable Intelligent Surfaces , 2020, IEEE Wireless Communications Letters.

[6]  H. Vincent Poor,et al.  Physics-Based Modeling and Scalable Optimization of Large Intelligent Reflecting Surfaces , 2020, IEEE Transactions on Communications.

[7]  Derrick Wing Kwan Ng,et al.  Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces , 2019, IEEE Journal on Selected Areas in Communications.

[8]  Qiang Cheng,et al.  Wireless Communications With Reconfigurable Intelligent Surface: Path Loss Modeling and Experimental Measurement , 2019, IEEE Transactions on Wireless Communications.

[9]  Mohamed-Slim Alouini,et al.  Smart Radio Environments Empowered by AI Reconfigurable Meta-Surfaces: An Idea Whose Time Has Come , 2019, ArXiv.

[10]  Shi Jin,et al.  Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI , 2018, IEEE Transactions on Vehicular Technology.

[11]  Qingqing Wu,et al.  Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming , 2018, IEEE Transactions on Wireless Communications.

[12]  A. Rukhin Matrix Variate Distributions , 1999, The Multivariate Normal Distribution.

[13]  T. A. Rahman,et al.  Modelling the Impact of Operating Frequencies on Path Loss and Shadowing Along Multi-Floor Stairwell for 0.7 GHz-2.5 GHz Range , 2014 .