A Hybrid Quantum-Classical Computing for Tracking Area Management for Future Networks

Tracking area (TA) planning is a critical component of mobility management in 5G and 6G networks, influencing handover efficiency, signaling overhead, and resource utilization. Existing approaches often rely on classical clustering and graph partitioning techniques, which may not fully capture complex network constraints or dynamic mobility patterns. In this paper, we present a hybrid quantum-classical solution for optimizing TA planning in 5G and 6G networks. The proposed solution applies first classical clustering to generate initial candidate TA groups using spectral and Louvain algorithms, capturing global structural patterns and community modularity in the network. These preliminary clusters are then refined through two quantum approaches: quantum spectral clustering, based on Laplacian eigenvectors, and quantum optimization formulated as a Quadratic Unconstrained Binary Optimization (QUBO) problem, solved using a Variational Quantum Algorithm with qubit-efficient encoding. By evaluating outcomes from both quantum tracks, the solution selects the configuration that best balances inter-TA handovers and network load. Evaluation on network datasets demonstrates that the hybrid approach outperforms traditional classical clustering, with QUBO-based quantum optimization reducing inter-TA handovers by up to 36.4% while also lowering the number of cells per TA and paging messages by 25% and 13.6%, respectively, compared to baseline methods. These results highlight the potential of hybrid quantum-classical methods for practical TA planning in next-generation mobile networks.