@article{badheka_sapis_khosravirad_viswanathan_2023, title={Accurate Modeling of Intelligent Reflecting Surface for Communication Systems}, volume={22}, ISSN={["1558-2248"]}, DOI={10.1109/TWC.2023.3237955}, abstractNote={In the conventional sense, a passive intelligent reflecting surface (IRS) is perceived as an ideal phase shifter to the incident signal. It is assumed that the phase of the incident signal can be altered to any desired value without affecting its magnitude. In this paper, we question the veracity of this assumption which forms the basis for the communication model that is widely used in the scientific community. Although there exist rigorous electromagnetic (EM) based models to analyze and design metasurfaces, the same cannot be said about its successor, intelligent reflecting surface. Therefore, we attempt to present an EM-based model that accurately describes intelligent scattering by any arbitrary-shaped IRS. Our objective in this paper is to bridge the gap between the fundamental EM formulation for an IRS and the communication model that accurately captures its functioning. We use Method-of-Moments (MoM), a computational electromagnetic approach to quantify the intelligent scattering by an arbitrary-shaped IRS. The proposed theoretical model is then validated with computational EM simulation in Feko. We then adopt the general MoM-based model for a special case where each IRS element is a center-loaded wire. Closed-form expressions for pathloss and beamwidth are derived considering free space propagation. We show analytically and numerically, that the received power predicted by the conventional model vs. what is observed through computational EM simulations can differ by 6 dB. Furthermore, we demonstrate that the impact of optimizing an IRS using the conventional model, where each element is treated as an ideal passive phase shifter, can result in an additional $6-8$ dB of power loss. As a final remark, we propose correction to the communication model that is currently used for IRS-aided networks when each IRS element is a center-loaded wire.}, number={9}, journal={IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS}, author={Badheka, Divyakumar and Sapis, Jakub and Khosravirad, Saeed R. and Viswanathan, Harish}, year={2023}, month={Sep}, pages={5871–5883} }
 @article{badheka_prasad_ma_piazzi_qi_2021, title={IRS Aided Communication Model for Compact MIMO Systems}, ISSN={["1550-3607"]}, DOI={10.1109/ICC42927.2021.9500647}, abstractNote={Traditionally intelligent reflecting surface (IRS) has been viewed as an ideal passive phase shifter with no mutually coupled elements. For this assumption to hold, IRS inter element spacing needs to be at least Nyquist spacing, thereby increasing overall aperture size with number of IRS elements. In this paper, we consider reverse problem wherein the aperture size is fixed. Here, packing more elements in a given space to enhance system performance results in mutual coupling and correlated fading. We consider an IRS aided communication network for a point-to-point (P2P) MIMO system with closely spaced transmit, receive and IRS antenna arrays. We derive channel models for various propagation scenarios and validate the theoretical expressions with actual antenna simulations. We allow for an arbitrary array configuration made from a material with finite conductivity. We show that such a communication network can be expressed as an equivalent MIMO system whose overall channel matrix is a non-linear function of IRS loading. The capacity expression is also a non-convex function of loads attached to IRS. We propose a proximal distance based algorithm to optimize capacity and analyze the effect of coupling aware optimization. Our results indicate capacity benefits on orders of 2-2.5 times for tightly coupled arrays when well designed optimization is performed using more accurate communication model that accounts for mutual coupling as opposed to naive methods that ignore coupling.}, journal={IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021)}, author={Badheka, Divyakumar and Prasad, Narayan and Ma, Zhengxiang and Piazzi, Leonard and Qi, Xiao-Feng}, year={2021} }