Digital Twin-Assisted Handover Scheme for Mobile Networks Using Generative AI

Handover management in mobile networks is challenged by high latency and reduced reliability in dense deployments and under user mobility. Here, existing schemes improve handover initiation by optimising the candidate handover at the decision time. However, these are applied after a non-negligible delay due to the control-plane signalling. Then, when applied, it may become invalid or degrade performance. To address this, we propose a Digital Twin (DT)-assisted handover scheme that performs predictive execution-time validation prior to the preparation of the Next Generation (NG)-based handover. To this end, the DT-What-If Generator (DT-WIG) is used to emulate short-horizon future network states under uncertainty. Here, the DT-WIG is a spatiotemporal graph generative model that uses variational latent sampling to generate counterfactual post-handover trajectories for the candidate handover decision. Then, the AMF estimates the failure and QoS risks associated with the candidate handover and approves/rejects it via standard-compliant signalling. With this, we form a policy-agnostic mechanism that runs on the underlying handover policy. Consequently, we evaluate performance using ns-3/5G-LENA trace generation and replay-based policy analysis, with OpenAirInterface-based signalling evaluation. The results show that the proposed method reduces the handover failure rate and handover interruption time while improving latency, jitter, throughput, and packet loss.