Malik Muhammad Saad

Accurate trajectory prediction and proactive Road-Side Unit (RSU) handover are essential for maintaining seamless connectivity and low-latency Multi-Edge Computing (MEC) based services in Cooperative Adaptive Cruise Control (CACC) systems. Conventionally, mobility forecasting and migration decisions are treated as separate problems, neglecting their inherent dependence, thus leading to premature or delayed handovers. In this work an integrated learning-based pipeline is introduced that first predicts the vehicle mobility using deep sequential models and the leverages these predictions to trigger intelligent RSU migrations. We employ a LSTM network to learn temporal dynamics from the large-scale FHWA dataset and generate multi-step displacement predictions over short horizons. These predictions are then used to infer future coverage boundaries and connectivity risks. Further, we model RSU migration as a binary-class classification task and train Random Forest (RF) and Multilayer Perceptron (MLP) as classifiers on engineered mobility features, such as velocities deltas, cumulative drift and distance to RSU markers. To handle skewed migration labels, we incorporate fallback heuristics and class-balanced strategies. Experiments on the 500,000 real vehicular samples reveal that the RF-based migration model achieves up to 73% accuracy. Meanwhile LSTM maintains stable short-horizon displacement accuracy with competitive Average Displacement Error (ADE) and Final Displacement Error (FDE) scores. Together, the models enable anticipatory RSU handover decisions that outperform naive threshold-based methods.

The evolution of 5G technology towards 5G-Advanced has introduced advanced vehicular applications with stringent Quality-of-Service (QoS) requirements. Addressing these demands necessitates intelligent resource management within the standard 3GPP network slicing framework. This paper proposes a novel resource management scheme leveraging a Deep Deterministic Policy Gradient (DDPG) algorithm implemented in the Network Slice Subnet Management Function (NSSMF). The scheme dynamically allocates resources to network slices based on real-time traffic demands while maintaining compatibility with existing infrastructure, ensuring cost-effectiveness. The proposed framework features a two-level architecture: the gNodeB optimizes slice-level resource allocation at the upper level, and vehicles reserve resources dynamically at the lower level using the 3GPP Semi-Persistent Scheduling (SPS) mechanism. Evaluation in a realistic, trace-based vehicular environment demonstrates the scheme’s superiority over traditional approaches, achieving higher Packet Delivery Ratio (PDR), improved Spectral Efficiency (SE), and adaptability under varying vehicular densities. These results underscore the potential of the proposed solution in meeting the QoS demands of critical 5G-Advanced vehicular applications.

The integration of Terrestrial Networks (TN) and Non-Terrestrial Networks (NTN) plays a crucial role in bridging the digital divide and enabling Sixth Generation (6G) and beyond to achieve truly ubiquitous connectivity. However, combining TN and NTN introduces significant energy challenges due to the diverse characteristics and operational environments of these systems. In this paper, we present for the first time a comprehensive overview of the design challenges associated with achieving Net-Zero energy targets in integrated TN and NTN systems. We outline a set of key enabling technologies that can support the energy demands of such networks while aligning with Net-Zero objectives. To enhance the Energy Efficiency (EE) of integrated TN and NTN systems, we provide a use case analysis that leverages Artificial Intelligence (AI) to deliver adaptable solutions across diverse deployment scenarios. Finally, we highlight promising research directions that can guide the sustainable evolution of integrated TN and NTN.

The Controller Area Network (CAN) acts as the backbone of intra-vehicle communication in modern Internet of Vehicles (IoV) systems, enabling real-time coordination among critical automotive subsystems. Despite its widespread adoption, CAN lacks essential security mechanisms such as encryption and message authentication, rendering it highly vulnerable to cyberattacks that can jeopardize vehicle safety and operational integrity. Developing an effective Few-Shot Learning (FSL)-based Intrusion Detection System (IDS) for CAN networks presents challenges due to data scarcity, noisy traffic, dynamic attack patterns, and the need for real-time efficiency. Existing FSL approaches often rely on deterministic models that struggle to capture the uncertainty and variability inherent in CAN network traffic. To address these challenges, we propose a Stochastic Prototypical Network based on a Random Neural Network (RaNN) for few-shot intrusion detection in CAN-based networks. RaNNs are inherently stochastic, enabling them to model uncertainty and variability in network traffic. By integrating RaNN with the prototypical network, the proposed framework computes stochastic prototypes that represent the distribution of normal and attack behaviors, improving robustness in noisy and dynamic environments. Additionally, the framework quantifies uncertainty in its predictions, enabling the system to flag ambiguous cases for further analysis, thereby reducing the risk of both false positives and negatives. The proposed approach demonstrates high classification performance across all FSL scenarios, achieving a maximum accuracy of 99.17% in a 15-shot configuration. The framework shows impressive computational efficiency with millisecond inference times and minimal training overhead, making it suitable for real-time deployment.

Rate-splitting multiple access (RSMA) emerges as a promising beyond 5G (B5G) network technology for ultrareliable low-latency communication (URLLC). It splits messages into private and common streams, and the common stream can be decoded by multiple receivers, allowing flexible signal interference management. Current RSMA schemes rely either on fixed preassigned or arbitrarily assigned transmission rates. However, this results in underutilization of network resources, reducing network efficiency. To address this limitation, we propose an application-specific RSMA, a feedback-based technique for URLLC in cell-free (CF) massive multiple-input, multiple-output (mMIMO) networks. The access points (APs) classify messages into traffic safety and nonsafety messages, based on the Third-Generation Partnership Project (3GPP) standard, to assign optimal transmission rates. Furthermore, the technique eliminates the need for a message combiner module, reducing the scheme’s complexity. A comparative analysis demonstrates promising improvements in the packet delivery ratio (PDR), minimized latency, and reduced computation complexity, which validates RSMA’s applicability in B5G for URLLC in CF mMIMO networks.

Cell-Free massive MIMO (CF-mMIMO) networks face significant challenges in achieving Ultra-Reliable Low-Latency Communication (URLLC) requirements due to inherent delays in content retrieval from central processing units (CPUs). This paper presents an integrated framework that jointly optimizes access point (AP) selection and content caching to minimize latency while maintaining reliability. We develop a novel content-aware user-centric clustering scheme that considers both cached content availability and channel conditions. The scheme features a Content Query Beacon (CQB) mechanism, which verifies content availability prior to connection establishment. To address the dynamic nature of content popularity, we design a novel proactive content caching strategy using Long Short-Term Memory (LSTM) to minimize CPU-dependent data retrieval. Extensive simulations demonstrate that our proposed framework achieves a 75% reduction in content delivery latency, 31.87% improvement in Quality of Experience (QoE), and a 26.8% increase in cache hit rates compared to conventional approaches. This comprehensive solution significantly enhances the capability of CF-mMIMO networks to deliver URLLC services, particularly in densely populated areas with diverse content demands.