Open-Source LLM-Driven Federated Transformer for Predictive IoV Management

The proliferation of connected vehicles within the Internet of Vehicles (IoV) ecosystem presents critical challenges in ensuring scalable, real-time, and privacy-preserving traffic management. Existing centralized IoV solutions often suffer from high latency, limited scalability, and reliance on proprietary Artificial Intelligence (AI) models, creating significant barriers to widespread deployment, particularly in dynamic and privacy-sensitive environments. Meanwhile, integrating Large Language Models (LLMs) in vehicular systems remains underexplored, especially concerning prompt optimization and effective utilization in federated contexts. To address these challenges, we propose the Federated Prompt-Optimized Traffic Transformer (FPoTT), a novel framework that leverages open-source LLMs for predictive IoV management. FPoTT introduces a dynamic prompt optimization mechanism that iteratively refines textual prompts to enhance trajectory prediction. The architecture employs a dual-layer federated learning paradigm, combining lightweight edge models for real-time inference with cloud-based LLMs to retain global intelligence. A Transformer-driven synthetic data generator is incorporated to augment training with diverse, high-fidelity traffic scenarios in the Next Generation Simulation (NGSIM) format. Extensive evaluations demonstrate that FPoTT, utilizing EleutherAI Pythia-1B, achieves 99.86% prediction accuracy on real-world data while maintaining high performance on synthetic datasets. These results underscore the potential of open-source LLMs in enabling secure, adaptive, and scalable IoV management, offering a promising alternative to proprietary solutions in smart mobility ecosystems.

Co-channel Interference Management for the Next-Generation Heterogeneous Networks using Deep Leaning

The connectivity of public-safety mobile users (MU) in the co-existence of a public-safety network (PSN), unmanned aerial vehicles (UAVs), and LTE-based railway networks (LRN) needs a thorough investigation. UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement for MU. The co-existence of heterogeneous networks gives rise to the issue of co-channel interference due to the utilization of the same frequency band. By considering both sharing and non-sharing of radio access channels (RAC), we analyze co-channel interference in the downlink system of PSN, UAV, and LRN. As the LRN control signal demands high reliability and low latency, we provide higher priority to LRN users when allocating resources from the LRN RAC shared with MUs. Moreover, UAVs are deployed at the cell edge to increase the performance of cell-edge users. Therefore, interference control techniques enable LRN, PSN, and UAVs to cohabit in a scenario of sharing RAC. By offloading more PSN UEs to the LRN or UAVs, the resource utilization of the LRN and UAVs BSs is enhanced. In this paper, we aim to adopt deep learning (DL) based on enhanced inter-cell-interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Among LRN, PSN BS, and UAVs, a DL-based coordinated multipoint (CoMP) link technique is utilized to enhance the performance of PSN MUs. Therefore, if radio access channels are shared, utilization of DL-based FeICIC and CoMP for coordinated scheduling gives the best performance.

Intelligent Random Access Framework for Massive and Ultra-Reliable Low Latency IoT Communications

The Internet of Things (IoT) enables smart cities to achieve the vision of connecting everything by smartly linking gadgets without the need for human interaction. However, due to the rapid proliferation of IoT devices, the amount of data produced accounts for a significant share of all communication services. Hence, 6G-enabled specifications for wireless networks are required to enable both massive and ultra-reliable low-latency access to such a hybrid IoT network. In this article, we propose a smart hybrid random access (SH-RA) scheme for massive connections and ultra-reliable low-latency access in the network architecture of IoT communications. According to numerical results, compared to the other baseline schemes, the SH-RA framework enormously enhances the total access probability and fulfills the quality-of-service (QoS) requirements.