联合睡眠模式激活与负载均衡的动态细胞负载：一种组合老虎机方法
Joint Sleep Mode Activation and Load Balancing with Dynamic Cell Load: A Combinatorial Bandit Approach
AI总结：
本文提出一种组合老虎机方法，用于在5G中通过动态细胞负载模型优化小型单元的睡眠模式激活与负载均衡，提升能效。
摘要：
我们提出了一种组合老虎机方法，以机会性地触发gNode-B（gNB）小型单元（SCs）的睡眠模式，随后进行基于小区范围扩展（CRE）的负载均衡过程。这通过确保第五代（5G）服务质量标识符（5QI）对用户设备（UEs）的需求得到维持来实现。关键挑战在于，停用给定的SC gNB会减少其自身的消耗，但可能增加邻近gNBs和宏gNB（覆盖小区）的负载，影响整体能效。这种现象通过建模动态细胞负载来准确刻画，该模型同时考虑UEs的位置、它们相对于所有SCs的相对位置以及数据需求。我们实验表明，所提出的组合上置信界（CUCB）后接负载均衡器在性能上优于不仅随意保持所有SCs开启的简单策略，也优于其他最先进的强化学习解决方案。所提出的算法可以作为开放无线电接入网络（O-RAN）近实时（NRT）RAN智能控制器（RIC）xApps实现。
英文摘要：
We propose a combinatorial bandit formulation to opportunistically trigger sleep modes in gNode-B (gNB) small cells (SCs), followed by a cell range expansion (CRE)-based load balancing procedure. This is implemented by ensuring that the fifth generation (5G) quality of service identifier (5QI)-requirements of user equipments (UEs) are maintained. The key challenge is the fact that while deactivating a given SC gNB reduces its own consumption, it may increase the load on neighboring gNBs and the macro gNB (coverage cell), impacting the overall energy efficiency. This phenomenon is accurately characterized by modeling the dynamic cell load that jointly takes into account the location of the UEs, their relative locations to all the SCs, and their data demands. We experimentally show that the proposed combinatorial upper confidence bound (CUCB) followed by the load balancer outperforms not only the naive strategies like arbitrarily keeping all the SCs on, but also other state-of-the-art reinforcement learning solutions. The proposed algorithm can be implemented as open-radio access network (O-RAN) near-real-time (NRT) RAN intelligent controller (RIC) xApps.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
