As B5G systems are evolving toward 6G, their coordination increasingly relies on AI-driven automation and orchestration actions, a process that is characterized as cognition. Therefore, a 6G system, through this cognitive process, acts as an intent-handling entity that comprehends sophisticated intent semantics from the users/tenants and calculates the ideal goal state for the specific intent, organizing the necessary adaptation actions that are needed for the transition of the system into that state. However, the use of cognitive-driven AI models to coordinate the purposes of a 6G system creates new risks, as a new surface of attack is born, where the whole 6G system operation may be maliciously affected by adversarial attacks within the user-intents. Focusing on this challenge, this paper realizes a prototype cognitive coordinator for 6G trustworthiness provision and investigates its adversarial robustness for different BERT-based quantification models, which are used for realizing the 6G cognitive system.

5G New Radio Vehicle-to-Everything (NR V2X) networks must support heterogeneous traffic with strict and diverse latency requirements. Conventional proportional-fair (PF) scheduling does not explicitly account for packet deadlines, which can lead to deadline violations for critical vehicular services under congestion. This paper studies deadline-aware MAC scheduler-weight adaptation for 5G NR V2X using probabilistic prediction and reinforcement learning. We implement a closed-loop ns-3/5G-LENA framework in which network telemetry is exchanged with a Python control agent through ns3-ai shared memory. Gaussian Mixture Model (GMM), Hidden Markov Model (HMM), and Bayesian Logistic Regression (BLR) classifiers are used to predict imminent deadline violations. Their outputs are either mapped directly to scheduler weights or provided as additional state information to a Proximal Policy Optimization (PPO) agent. We evaluate ten scheduling strategies: PF, a non-learning Slack-Based Deadline-Aware Scheduler (SB-DAS), three classifier-only controllers, three classifier-assisted PPO variants, PPO-only, and PPO-only with safety shielding. Experiments are conducted across three vehicle densities and three random seeds per density, using the Deadline-Constrained Packet Reception Ratio (DC-PRR) as the main metric. The PF baseline achieves 61.55% mean DC-PRR and degrades from 75.2% at 30 vehicles to 44.1% at 60 vehicles. In contrast, all adaptive strategies exceed 95% mean DC-PRR and recover 34–38 percentage points over PF in every paired density/seed comparison. The main result is therefore the robust gap between PF and deadline-aware adaptation. Differences among the adaptive controllers are much smaller and fall within the observed seed-to-seed variability. In particular, SB-DAS, which uses no classifier, neural network, or training, achieves DC-PRR statistically indistinguishable from the learned and probabilistic controllers. This indicates that, in the evaluated scenarios, most of the gain comes from deadline awareness itself rather than from learning. We also find that adding classifier-derived violation probabilities to PPO does not consistently improve performance over PPO using raw telemetry alone. To support reproducibility and deployment assessment, the paper includes detailed parameter tables, reward-coefficient and sensitivity analysis, scheduler-weight sensitivity, and per-controller inference-latency and complexity measurements.

As B5G systems are evolving towards 6G, their coordination increasingly relies on AI-driven automation and orchestration actions, which is a process that is characterized as cognition. Therefore, a 6G system through this cognitive process, acts as an in-tent-handling entity that comprehends sophisticated intent semantics from the us-ers/tenants and calculates the ideal goal state for the specific intent, organizing the neces-sary adaptation actions that are needed for the transition of the system into that state. However, the use of cognitive-driven AI models for coordinating purposes of a 6G system creates new risks, since a new surface of attack is born, where the whole 6G system opera-tion may be maliciously affected by adversarial attacks within the user-intents. Focusing on this challenge, this paper realizes a protype cognitive coordinator for 6G trustworthi-ness provision and investigates its adversarial robustness for different BERT-based quan-tification models, which are used for realizing the 6G cognitive system.

The shift toward autonomous driving is steadily reducing the need for human intervention in vehicular operations, but sustaining this shift depends on 6G networks that can reliably multiplex diverse, delay-critical services under deterministic latency, throughput, and reliability constraints. Static over-provisioning can meet the stringent Quality of Service (QoS) requirements of critical traffic, but at the cost of resource starvation for co-existing non-critical services, and it degrades further under adverse channel conditions or network congestion where spectrum must be used efficiently. This motivates dynamic, service-aware scheduling as a core requirement for multi-service 6G vehicular architectures. This paper presents SOVANET+, an extended scheduling technique that jointly accounts for service criticality (critical vs. non-critical), network load, and wireless link quality to allocate resources adaptively across coexisting Vehicle-to-Everything (V2X) services. We evaluate SOVANET+ through extensive simulations of a congested single-cell urban-grid deployment supporting delay-critical automated driving services. Results show that SOVANET+ achieves lower latency and jitter, higher throughput, and improved uplink and downlink reliability compared to existing scheduling approaches, while scaling effectively to large numbers of connected autonomous vehicles, supporting its viability for next-generation intelligent transportation systems.