Conformal IRS-Empowered MIMO-OFDM: Channel Estimation and Environment Mapping

We consider the channel estimation and environment mapping problems in multiple-input multiple-output orthogonal frequency division multiplexing systems empowered by intelligent reconfigurable surfaces (IRSs). In order to acquire more in-depth environmental information, as well as, to flexibly take into account existing real-life infrastructure, we propose a novel three-dimensional conformal IRS architecture consisting of reflective unit cells distributed on curved surfaces. We model the training signal as a third-order canonical polyadic tensor and construct a tensor factorization problem. Given specific conditions on the allocated temporal-frequency training resources, we develop four channel estimation approaches, i.e., least squares, direct, wideband direct and wideband subspace methods, by leveraging tensor techniques and nonlinear system solvers. By fully exploiting the characteristics of conformal IRSs, we propose two decoupling modes to precisely recover the multipath parameters without ambiguities, which cannot be supported by the traditional IRS planar topologies. We implement scatterer mapping and user positioning tasks based on precise parameter estimates. Simulation results indicate that the proposed conformal IRS structure and estimation schemes can recover the channel state information with remarkable accuracy, thereby offering a centimeter-level resolution of environment mapping.