用智能筛选方法高效选出最优用户调度组合，提升5G通信效率。
Challenger-Based Combinatorial Bandits for Subcarrier Selection in OFDM Systems
- 设计双短名单机制，动态筛选潜在最优用户组和竞争者。
- 相比现有方法减少计算量，识别准确率超95%且支持实时调整。
- 适合需要快速响应的智能通信系统，如5G基站资源调度。
本文研究多用户MIMO下行链路中识别前m个最优用户调度集合的问题，将其建模为随机线性强化学习中的组合纯探索问题。由于动作空间呈指数级增长，穷举搜索不可行。为此，采用线性效用模型以实现高效探索和可靠候选集选择。提出一种基于差距索引（gap-index）的框架，维护当前冠军臂（前m个最优集合）的短名单，以及一个轮换的挑战者臂短名单，这些挑战者对冠军构成最大威胁。该设计聚焦于最具信息量的差距索引比较，显著降低运行时间和计算开销，同时保持高识别精度。方法还揭示了速度与精度之间的可调权衡。在真实OFDM下行链路环境下的仿真表明，短名单驱动的纯探索使在线、测量高效的子载波选择在人工智能赋能的通信系统中成为可能。
原文摘要 · Abstract (English)
This paper investigates the identification of the top-m user-scheduling sets in multi-user MIMO downlink, which is cast as a combinatorial pure-exploration problem in stochastic linear bandits. Because the action space grows exponentially, exhaustive search is infeasible. We therefore adopt a linear utility model to enable efficient exploration and reliable selection of promising user subsets. We introduce a gap-index framework that maintains a shortlist of current estimates of champion arms (top-m sets) and a rotating shortlist of challenger arms that pose the greatest threat to the champions. This design focuses on measurements that yield the most informative gap-index-based comparisons, resulting in significant reductions in runtime and computation compared to state-of-the-art linear bandit methods, with high identification accuracy. The method also exposes a tunable trade-off between speed and accuracy. Simulations on a realistic OFDM downlink show that shortlist-driven pure exploration makes online, measurement-efficient subcarrier selection practical for AI-enabled communication systems.
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