发表机构
Nokia Technologies; Nokia Bell Labs（诺基亚技术; 诺基亚贝尔实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过集成链路级与系统级模拟器，组合多种上行AI功能（如深度学习MIMO接收机、强化学习功率控制和链路自适应），在真实多小区部署中实现约27%的平均用户吞吐量提升。
AI 中文摘要
6G的主要变革因素之一将是人工智能（AI）的集成，使其成为无线接入网（RAN）的原生组成部分。虽然迄今为止，大多数物理层AI功能都是使用链路级模拟单独评估的，但它们在真实多小区、多用户设备（UE）部署中的组合行为在很大程度上仍未得到探索。在本文中，我们展示了当多个上行AI功能同时启用时的系统级性能结果，这是通过集成精确的链路级和系统级模拟器实现的。为了在真实上行调度器产生的动态分配下推断最先进的深度学习辅助多输入多输出（MIMO）接收机，我们提出了一种镜像数据增强方法，该方法将接收机性能与调度分配大小解耦。除了这些物理层（PHY）接收机功能外，我们还结合了深度强化学习方面的几项近期进展，以训练上行功率控制和链路自适应，这些性能优于启发式基线，并进一步提升了仅由AI接收机可获得的增益。系统级结果表明，与非AI基线相比，组合的AI功能将平均上行用户吞吐量提高了约27%，证实了各个PHY和媒体接入控制（MAC）AI功能在联合部署时提供了互补的增益。
英文摘要
One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network (RAN). While most physical-layer AI features have so far been evaluated in isolation using link-level simulations, their combined behavior in a realistic multi-cell, multi-UE deployment has remained largely unexplored. In this paper, we present system-level performance results when multiple uplink AI features are enabled together, achieved by integrating accurate link-level and system-level simulators. To infer state-of-the-art deep-learning-aided Multiple Input Multiple Output (MIMO) receivers under the dynamic allocations produced by a realistic uplink scheduler, we propose a mirrored data augmentation method that decouples receiver performance from scheduled allocation size. In addition to these Physical Layer (PHY) receiver features, we combine several recent advances in deep reinforcement learning to train uplink power control and link adaptation that outperform a heuristic baseline and further boost the gains obtainable from the AI receiver alone. The system-level results show that the combined AI features improve the mean uplink user throughput by roughly 27% compared to a non-AI baseline, confirming that the individual PHY and Medium Access Control (MAC) AI features provide complementary gains when deployed jointly.
CommentsThis work has been submitted to IEEE for consideration for publication
