Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization
This addresses the need for seamless mobility service in network-sliced environments, offering a domain-specific incremental improvement over existing methods.
The paper tackled the problem of mobility robustness optimization in next-generation networks with network slicing, proposing a deep reinforcement learning approach that improves handover performance with per-slice service assurance, showing significant improvements in slice-aware service continuation compared to legacy algorithms.
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.