Indrakshi Dey

Yan Zhang, Indrakshi Dey, Nicola Marchetti

Reconfigurable Intelligent Surfaces (RIS) are known as a promising technology to improve the performance of wireless communication networks, and have been extensively studied. Movable Antennas (MA) are a novel technology that fully exploits the antenna placement for enhancing the system performance. This article aims at evaluating the impact of transmit power and number of antenna elements on the outage probability performance of an MA-enabled RIS structure (MA-RIS), compared to existing Fixed-Position Antenna RIS (FPA-RIS). The change in geometry caused by the movement of antennas and its implications for the effective number of illuminated elements, are studied for 1D and 2D array structures. Our numerical results confirm the performance advantage provided by MA-RIS, achieving 24\% improvement in outage probability, and 2 dB gain in Signal-to-Noise Ratio (SNR), as compared to FPA-RIS.

Mohamed Khalafalla Hassan, Indrakshi Dey

Redundancy is widely used to sustain service continuity in programmable and virtualized networks; however, replicated functions often share platforms, software stacks, and control dependencies, making them vulnerable to correlated failures. Consequently, replica counts alone may overestimate true resilience. This paper adopts a degeneracy-aware perspective, where robustness depends on the availability of structurally diverse yet functionally equivalent alternatives. We formalize this perspective through three complementary metrics: the Functional Substitution Score (FSS), which quantifies structurally distinct substitutes for a function; the Algorithmic Resilience Quotient (ARQ), which measures diversity among algorithms that remain comparable in delivered performance; and the Multi-Layer Degeneracy Index (MLDI), which captures how functional diversity is distributed across architectural layers. Using targeted disruption protocols on a synthesized data, we show that redundancy and robustness can diverge substantially. The results show that FSS separates structural diversity from replica count, ARQ distinguishes genuine algorithmic alternatives from near-duplicate implementations, and MLDI captures cross-layer buffering that remains hidden under redundancy-only analysis. These findings establish degeneracy as a practical resilience primitive for open, disaggregated, and virtualized 6G systems.

Rahul Umesh Mhapsekar, Ilias Cherkaoui, Lizy Abraham et al.

Modern AI systems are increasingly deployed under non-stationary computational, demographic, and operational conditions in which static resource allocation strategies degrade both predictive performance and human-centric properties such as fairness and explainability. This paper presents AURORA-AI, an Adaptive Utility-driven Resource Orchestration framework for Resilient AI that unifies Hamilton-Jacobi-Bellman feedback control, Lyapunov-based stability monitoring, and a fairness-aware composite utility into a single closed-loop policy.The framework continuously redistributes computational budget across a population of heterogeneous AI models so that the global utility, defined jointly over predictive performance, demographic parity, cost, latency, robustness, and interpretability, remains maximised under disruption. The framework is evaluated in a stress-rich discrete-time simulation that concurrently injects demographic bias shocks, gradual concept drift, and abrupt black-swan disruptions, and is compared against five established controllers including Static, Round Robin, Greedy, LinUCB, and a deep reinforcement-learning agent based on Proximal Policy Optimisation. AURORA-AI achieves immediate recovery from the black-swan event compared to eighty-eight time steps for the Static baseline and twenty-two for Proximal Policy Optimisation, lifts the alpha-quantile and the super-quantile by twenty-nine and twenty-five percent respectively, simultaneously reduces the mean and maximum demographic parity gap, and increases the fraction of Lyapunov-stable operating steps. These results indicate that fairness-aware adaptive orchestration grounded in stability theory is a practical and theoretically motivated path toward resilient human-centric AI deployment.

Surya Jayakumar, Kieran Sullivan, John McLaughlin et al.

This study introduces a novel data-driven framework and the first-ever county-scale application of Spatio-Temporal Graph Neural Networks (STGNN) to forecast composite sustainability indices from herd-level operational records. The methodology employs a novel, end-to-end pipeline utilizing a Variational Autoencoder (VAE) to augment Irish Cattle Breeding Federation (ICBF) datasets, preserving joint distributions while mitigating sparsity. A first-ever pillar-based scoring formulation is derived via Principal Component Analysis, identifying Reproductive Efficiency, Genetic Management, Herd Health, and Herd Management, to construct weighted composite indices. These indices are modelled using a novel STGNN architecture that explicitly encodes geographic dependencies and non-linear temporal dynamics to generate multi-year forecasts for 2026-2030.