Exact Statistical Characterization and Performance Analysis of Fluid Reconfigurable Intelligent Surfaces

Fluid reconfigurable intelligent surfaces (FRIS) extend conventional RIS architectures by enabling physical reconfiguration of element positions, thereby introducing a fundamentally new degree of freedom for controlling spatial correlation and improving link reliability. Despite this promise, rigorous performance analysis of FRIS-assisted wireless systems has remained challenging, as exact statistical analyses of the end-to-end cascaded channels have been unavailable. This paper addresses this gap by providing the first exact closed-form characterization of the end-to-end cascaded channel gain in FRIS-aided systems under general spatial correlation. By exploiting the spectral structure of the FRIS-induced correlation matrix, we show that the channel gain statistics can be represented as a finite linear combination of K-distributions. This unified formulation naturally captures fully correlated, effectively decorrelated, and intrinsically uncorrelated operating regimes as special cases. Building on the derived channel statistics, we further obtain exact closed-form expressions for the outage probability and ergodic capacity. We also conduct an outage-based asymptotic analysis, which reveals the true diversity order of the system. Numerical results corroborate the proposed analytical framework via Monte Carlo simulations, benchmark its accuracy against state-of-the-art approximation-based approaches, and demonstrate that fluidic reconfiguration can yield tangible reliability gains by reshaping the spatial correlation structure.

PAPR Reduction in OFDM Systems Using Neural Networks: A Case Study on the Importance of Dataset Generalization

In [1], we introduced a NN designed to reduce the PAPR in OFDM systems. However, the original study did not include explicit generalization tests to assess how well the NN would perform on previously unseen data, which prevented a comprehensive evaluation of the model's robustness and applicability in diverse scenarios. To address this gap, we conducted additional generalization assessments, the results of which are presented in this case study. These results serve both to complement and to refine the original analysis reported in [1]. Most importantly, the overall conclusions of the initial study remain valid: the NN is still able to reduce the PAPR level to a desired reference value, also with a lower computational cost, confirming the effectiveness and practical applicability of the proposed method across a more generalized setting.