mlsanityData to 7 Oct 2026

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

arXiv 2203.03227 ↗First posted 7 Mar 2022cs.NI cs.AI cs.LGcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 4 of its 18 references that are in the map. First posted on arXiv 7 Mar 2022.

Builds on

4 of 18 references.

References · 4

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  2. 2015Deep Reinforcement Learning in Large Discrete Action Spacescited by 158
  3. 2018Deep Energy Estimator Networkscited by 34
  4. 2020Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfigurationcited by 7

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Offline Reinforcement Learning for Mobility Robustness Optimizationcited by 1
  2. 2025Compositional Learning for Modular Multi-Agent Self-Organizing Networkscited by 0

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