Dynamic Scheduling for Enhanced Performance in RIS-assisted Cooperative Network with Interference

Reconfigurable Intelligent Surfaces (RIS) have emerged as transformative technologies, enhancing spectral efficiency and improving interference management in multi-user cooperative communications. This paper investigates the integration of RIS with Flexible-Duplex (FlexD) communication, featuring dynamic scheduling capabilities, to mitigate unintended external interference in multi-user wireless networks. By leveraging the reconfigurability of RIS and dynamic scheduling, we propose a user-pair selection scheme to maximize system throughput when full channel state information (CSI) of interference is unavailable. We develop a mathematical framework to evaluate the throughput outage probability when RIS introduces spatial correlation. The derived analytical results are used for asymptotic analysis, providing insights into dynamic user scheduling under interference based on statistical channel knowledge. Finally, we compare FlexD with traditional Full Duplex (FD) and Half Duplex (HD) systems against RIS-assisted FlexD. Our results show FlexD's superior throughput enhancement, energy efficiency and data management capability in interference-affected networks, typical in current and next-generation cooperative wireless applications like cellular and vehicular communications.

Gaussian random field approximation via Stein's method with applications to wide random neural networks

We derive upper bounds on the Wasserstein distance ($W_1$), with respect to $\sup$-norm, between any continuous $\mathbb{R}^d$ valued random field indexed by the $n$-sphere and the Gaussian, based on Stein's method. We develop a novel Gaussian smoothing technique that allows us to transfer a bound in a smoother metric to the $W_1$ distance. The smoothing is based on covariance functions constructed using powers of Laplacian operators, designed so that the associated Gaussian process has a tractable Cameron-Martin or Reproducing Kernel Hilbert Space. This feature enables us to move beyond one dimensional interval-based index sets that were previously considered in the literature. Specializing our general result, we obtain the first bounds on the Gaussian random field approximation of wide random neural networks of any depth and Lipschitz activation functions at the random field level. Our bounds are explicitly expressed in terms of the widths of the network and moments of the random weights. We also obtain tighter bounds when the activation function has three bounded derivatives.