  The legacy mobility robustness optimization (MRO) in self-organizing networks
aims at improving handover performance by optimizing cell-specific handover
parameters. However, such solutions cannot satisfy the needs of next-generation
network with network slicing, because it only guarantees the received signal
strength but not the per-slice service quality. To provide the truly seamless
mobility service, we propose a deep reinforcement learning-based slice-aware
mobility robustness optimization (SAMRO) approach, which improves handover
performance with per-slice service assurance by optimizing slice-specific
handover parameters. Moreover, to allow safe and sample efficient online
training, we develop a two-step transfer learning scheme: 1) regularized
offline reinforcement learning, and 2) effective online fine-tuning with mixed
experience replay. System-level simulations show that compared against the
legacy MRO algorithms, SAMRO significantly improves slice-aware service
continuation while optimizing the handover performance.

Networking and Internet Architecture;Artificial Intelligence;Machine Learning