AI-Driven Fuzzing for Vulnerability Assessment of 5G Traffic Steering Algorithms

Traffic Steering (TS) dynamically allocates user traffic across cells to enhance Quality of Experience (QoE), load balance, and spectrum efficiency in 5G networks. However, TS algorithms remain vulnerable to adversarial conditions such as interference spikes, handover storms, and localized outages. To address this, an AI-driven fuzz testing framework based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is proposed to systematically expose hidden vulnerabilities. Using NVIDIA Sionna, five TS algorithms are evaluated across six scenarios. Results show that AI-driven fuzzing detects 34.3% more total vulnerabilities and 5.8% more critical failures than traditional testing, achieving superior diversity and edge-case discovery. The observed variance in critical failure detection underscores the stochastic nature of rare vulnerabilities. These findings demonstrate that AI-driven fuzzing offers an effective and scalable validation approach for improving TS algorithm robustness and ensuring resilient 6G-ready networks.

FairShare: Auditable Geographic Fairness for Multi-Operator LEO Spectrum Sharing

Dynamic spectrum sharing (DSS) among multi-operator low Earth orbit (LEO) mega-constellations is essential for coexistence, yet prevailing policies focus almost exclusively on interference mitigation, leaving geographic equity largely unaddressed. This work investigates whether conventional DSS approaches inadvertently exacerbate the rural digital divide. Through large-scale, 3GPP-compliant non-terrestrial network (NTN) simulations with geographically distributed users, we systematically evaluate standard allocation policies. The results uncover a stark and persistent structural bias: SNR-priority scheduling induces a 1.65x urban-rural access disparity, privileging users with favorable satellite geometry. Counter-intuitively, increasing system bandwidth amplifies rather than alleviates this gap, with disparity rising from 1.0x to 1.65x as resources expand. To remedy this, we propose FairShare, a lightweight, quota-based framework that enforces geographic fairness. FairShare not only reverses the bias, achieving an affirmative disparity ratio of Delta_geo = 0.72x, but also reduces scheduler runtime by 3.3%. This demonstrates that algorithmic fairness can be achieved without trading off efficiency or complexity. Our work provides regulators with both a diagnostic metric for auditing fairness and a practical, enforceable mechanism for equitable spectrum governance in next-generation satellite networks.

Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resilience

Adaptive beam switching in 6G networks is challenged by high frequencies, mobility, and blockage. We propose an Online Learning framework using Deep Reinforcement Learning (DRL) with an enhanced state representation (velocity and blockage history), a GRU architecture, and prioritized experience replay for real-time beam optimization. Validated via Nvidia Sionna under time-correlated blockage, our approach significantly enhances resilience in SNR, throughput, and accuracy compared to a conventional heuristic. Furthermore, the enhanced DRL agent outperforms a reactive Multi-Armed Bandit (MAB) baseline by leveraging temporal dependencies, achieving lower performance variability. This demonstrates the benefits of memory and prioritized learning for robust 6G beam management, while confirming MAB as a strong baseline.