论文标题
在深度强化学习中的知识转移，以进行切片感知流动性鲁棒性优化
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization
论文作者
论文摘要
自组织网络中的传统移动性鲁棒性优化（MRO）旨在通过优化细胞特异性的切换参数来提高切换性能。但是，这种解决方案无法通过网络切片来满足下一代网络的需求，因为它只能保证接收到的信号强度，而不能保证每片服务的服务质量。为了提供真正的无缝移动性服务，我们建议通过优化基于深度增强学习的切片稳健性优化（SAMRO）方法，该方法通过优化特定于切片的切换参数来通过每板利式服务的保证来提高移交性能。此外，为了允许安全有效的在线培训，我们开发了两步转移学习方案：1）正规化离线增强学习，以及2）有效的在线微调，并具有混合体验重播。系统级模拟表明，与传统MRO算法相比，SAMRO显着改善了切片感知的服务延续，同时优化了切换性能。
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.
