Accelerated Recovery with RIS: Designing Wireless Resilience in Mission-Critical Environments
Kevin Weinberger, Robert-Jeron Reifert, Aydin Sezgin, Mehdi Bennis
TL;DR
This work addresses resilience in mission-critical wireless networks by introducing a framework that explicitly quantifies adaptability through gradient augmentation of the rate function and by integrating RIS to provide alternative propagation paths. The main approach combines a resilience metric with a multi-objective optimization that enforces rapid adaptation via the rate-gradient $\nabla_{\mathbf{v}} r_k^{\text{RIS}}$, while using RIS for redundancy and phase-shift control. Key contributions include (i) a resilience metric based on absorption, adaptation, and time-to-recovery, (ii) RIS-enabled resiliency mechanisms to expand channel paths and control, and (iii) a resilience-guided alternating optimization that leverages SCA for beamforming and phase-shift design with one-iteration updates for fast response. Numerical results demonstrate that gradient augmentation yields faster and more robust adaptation to blockages, enabling higher resilience $r$ in dynamic, interference-prone environments with scalable RIS sizes, which is crucial for reliable 6G+ operations in mission-critical settings.
Abstract
As 6G and beyond redefine connectivity, wireless networks become the foundation of critical operations, making resilience more essential than ever. With this shift, wireless systems cannot only take on vital services previously handled by wired infrastructures but also enable novel innovative applications that would not be possible with wired systems. As a result, there is a pressing demand for strategies that can adapt to dynamic channel conditions, interference, and unforeseen disruptions, ensuring seamless and reliable performance in an increasingly complex environment. Despite considerable research, existing resilience assessments lack comprehensive key performance indicators (KPIs), especially those quantifying its adaptability, which are vital for identifying a system's capacity to rapidly adapt and reallocate resources. In this work, we bridge this gap by proposing a novel framework that explicitly quantifies the adaption performance by augmenting the gradient of the system's rate function. To further enhance the network resilience, we integrate Reconfigurable Intelligent Surfaces (RISs) into our framework due to their capability to dynamically reshape the propagation environment while providing alternative channel paths. Numerical results show that gradient augmentation enhances resilience by improving adaptability under adverse conditions while proactively preparing for future disruptions.