Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization

• URL: http://arxiv.org/abs/2203.03227v1
• Date: Mon, 7 Mar 2022 09:26:15 GMT
• Title: Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization
• Authors: Qi Liao and Tianlun Hu and Dan Wellington
• Abstract summary: We propose a deep reinforcement learning-based slice-aware mobility robustness optimization (SAMRO) approach. It improves handover performance with per-slice service assurance by optimizing slice-specific handover parameters. We also develop a two-step transfer learning scheme: 1) regularized offline reinforcement learning, and 2) effective online fine-tuning with mixed experience replay.
• Score: 0.8937905773981699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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