A Comprehensive Survey of Quantum Computing Algorithms for Handover Optimization in 5G HetNet
TL;DR
This survey provides a comprehensive overview of quantum computing and quantuminspired algorithms for handover optimization in 5G HetNets and examines their potential to address multidimensional handover optimization problems involving signal quality, user mobility, network load balancing, throughput, latency, energy efficiency, handover failure rate, and QoS requirements.
Abstract
Fifth-generation (5G) heterogeneous networks (HetNets), with ultra-dense small-cell deployments, diverse radio access technologies, and high user mobility, are among the most rapidly evolving infrastructures today. Hence, effective handover management has evolved as an essential requirement for sustaining seamless connectivity, Quality of Service (QoS), and user experience. Conventional handover optimization techniques are generally limited because of high latency, excessive signaling overhead, frequent handover failures, the ping-pong effect, and poor decision-making in a highly dynamic network environment. Recent advancements in quantum computing have opened new opportunities to address these challenges by offering powerful computational and optimization techniques. This survey provides a comprehensive overview of quantum computing and quantuminspired algorithms for handover optimization in 5G HetNets. In particular, it discusses the principles and methodologies of prominent approaches, such as quantum annealing, the Grover search algorithm, Quantum Genetic Algorithms (QGA), Quantum Particle Swarm Optimization (QPSO), quantum- inspired evolutionary algorithms, and Quantum Machine Learning (QML). It further examines their potential to address multidimensional handover optimization problems involving signal quality, user mobility, network load balancing, throughput, latency, energy efficiency, handover failure rate, and QoS requirements. A comparative analysis of the existing studies is provided to evaluate algorithm performance, computational complexity, and scalability for practical deployment in real-world 5G environments. The survey also emphasizes the integration of quantum computing with Artificial Intelligence (AI) and Machine Learning (ML) frameworks for intelligent mobility management. Finally, it highlights some significant research challenges, implementation issues, hardware limitations, and future directions for exploiting quantum technologies in 5G/6G mobility management systems.