This paper introduces TH-GCN, a Graph Convolutional Network-based approach for handover management in dense 5G vehicular networks, which models dynamic network conditions to reduce handovers by up to 78% and improve signal quality and throughput through real-time, topology-aware decisions.
Graph Data, GNN, Efficiency, Prediction, Multimodal Systems
Nazanin Mehregan, Robson E. De Grande
Brock University, Canada
Generated by grok-3
Background Problem
The rapid deployment of 5G networks in vehicular environments has introduced significant challenges in handover management due to the high mobility of vehicles, limited coverage of 5G mmWave signals, and the dense deployment of small cells, leading to frequent handovers and the ‘ping-pong effect.’ These issues degrade network stability, throughput, and quality of service (QoS) in urban settings, particularly for real-time applications in Intelligent Transportation Systems (ITS). This paper aims to address these problems by optimizing handover decisions to minimize unnecessary transitions and maximize throughput and signal quality in dense 5G vehicular networks.
Method
The proposed TH-GCN (Throughput-oriented Graph Convolutional Network) leverages Graph Neural Networks (GNNs) to model 5G vehicular networks as dynamic graphs, where vehicles and base stations are nodes with features like speed, direction, position, and load, and edges represent relationships weighted by throughput, signal quality, and distance. The core idea is to use GCNs for real-time handover optimization by capturing spatial and temporal dependencies through message passing and node embedding generation. The main steps include: (1) constructing dynamic graphs from simulated data using tools like Omnetpp and Simu5G; (2) employing a GCN architecture with edge-weighted aggregation to prioritize high-quality connections; (3) training the model incrementally with a triplet loss function to optimize embeddings for tower selection, ensuring vehicles are closer to optimal towers in the embedding space; and (4) ranking candidate towers during inference using similarity scores (e.g., cosine similarity) for handover decisions, incorporating SINR checks with hysteresis thresholds to avoid unnecessary transitions. This dual-centric approach integrates both user equipment and base station perspectives for multi-objective optimization.
Experiment
The experiments were conducted using a simulation framework combining INET, Simu5G, VEINS, SUMO, and OMNet++, based on a real-world map of Cologne, Germany, with 10 gNodeB towers and vehicle densities ranging from 100 to 1000. The setup emulated VoIP uplink traffic to measure metrics like average SINR, throughput, packet transmission rate, packet loss ratio, handovers, and ping-pong handovers, averaged over 10 runs with 95% confidence intervals. Results show TH-GCN outperforming baseline and CO-SRL methods, achieving up to a 78% reduction in handovers, a 10% improvement in SINR, and higher throughput, especially at high densities, due to its graph-based load balancing and embedding optimization. However, it exhibited a lower packet transmission rate at higher densities, indicating a trade-off prioritizing successful delivery over speed. While the experimental design is comprehensive for an urban scenario, it lacks diversity in testing environments (e.g., rural or sparse tower settings), and the absence of real-world data limits validation of the model’s practical applicability. The setup is reasonable for initial evaluation but not exhaustive enough to claim broad superiority without addressing generalizability concerns.
Further Thoughts
The TH-GCN approach opens up interesting avenues for integrating graph-based methods into real-time network management, particularly in the context of 5G and beyond. However, its reliance on static graph snapshots limits its predictive power for future network states, an area where temporal GNN variants (e.g., combining GCN with recurrent structures) could enhance performance, as hinted by the authors’ future work. Additionally, the trade-off between packet transmission rate and throughput raises questions about its suitability for latency-critical applications like autonomous driving, where consistent packet timing might be more crucial than raw throughput. This connects to broader research in edge computing, where deploying lightweight GNN models on resource-constrained devices could further reduce latency, aligning with trends in federated learning for privacy-preserving, decentralized network optimization. Another insight is the potential synergy with multi-agent reinforcement learning, where each vehicle or tower could act as an agent learning local handover policies while coordinating globally via graph embeddings, potentially addressing scalability issues in ultra-dense networks. These connections suggest that while TH-GCN is a promising step, its real-world impact hinges on addressing dynamic adaptability and application-specific trade-offs.