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.

翻译：本文提出一种组合多臂赌博机框架，用于在gNode-B（gNB）小基站中机会性地触发睡眠模式，并随后执行基于小区范围扩展的负载均衡过程。该方案通过确保用户设备的第五代服务质量标识符要求得到满足来实现。核心挑战在于：停用特定小基站gNB虽能降低其自身能耗，却可能增加相邻gNB及宏基站gNB（覆盖小区）的负载，从而影响整体能效。本研究通过建立动态小区负载模型精确刻画该现象，该模型综合考虑了用户设备的地理位置、其相对于所有小基站的相对位置及其数据需求。实验结果表明，所提出的组合置信上界算法结合负载均衡器的方案，不仅优于保持所有小基站持续开启的简单策略，也优于其他先进的强化学习解决方案。该算法可作为开放无线接入网近实时无线接入网络智能控制器中的xApp应用实现。