面向轻量级O-DU dApp的基于上行CSI的自适应下行DM-RS分配
Utility-Aware Adaptive Downlink DM-RS Allocation from Uplink CSI for Lightweight O-DU dApps
AI总结：
本文提出一种基于上行CSI的轻量级效用感知DM-RS分配控制器，通过11-128-64-32-6多层感知器选择PDSCH DM-RS配置，在多种OOD条件下显著提升吞吐量，并实现微秒级推理与亚10毫秒闭环。
AI中文摘要：
5G新无线电（NR）中的自适应解调参考信号（DM-RS）配置在导频开销与信道跟踪鲁棒性之间进行权衡，使得单一的静态密度在异构移动性和传播环境下表现次优。本文研究了一种轻量级的每用户（per-UE）控制器，旨在用于O-DU侧分布式应用（dApp）执行。该控制器消耗十一个可部署的上行CSI统计量，并从六种标准衍生的PDSCH DM-RS配置中选择。我们不追求oracle级精度，而是训练一个11-128-64-32-6多层感知器，采用效用感知目标，保留所有候选动作的吞吐量结构。在覆盖294种信道/速度/信噪比条件和7,056个观测的大规模轨迹不相交评估中，所提出的U-SoftCE策略在分布内、CDL信道分布外（OOD）和300公里/小时移动性OOD分割上，相对于训练选择的最佳固定模式，吞吐量分别显著提高了1.30、0.86和1.13个百分点；配对95%自助法区间在三种情况下均排除零。一项独立的比特精确Sionna NR LDPC验证，在36个保留条件和每个候选50个传输块上，保持了+1.21个百分点的增益，95%聚类自助法区间为[0.27, 2.13]。原生C++实现使用12,070个稠密参数，平均推理时间为5.59微秒，而基于FlexRIC的仿真O-DU原型展示了亚10毫秒的闭环操作。这些结果支持效用感知目标用于紧凑的自适应PHY控制器，同时揭示了精确类精度优化的局限性。
英文摘要：
Adaptive demodulation reference signal (DM-RS) placement in 5G New Radio (NR) trades pilot overhead against channel-tracking robustness, making a single static density suboptimal across heterogeneous mobility and propagation regimes. This paper studies a lightweight per-UE controller intended for O-DU-side distributed application (dApp) execution. The controller consumes eleven deployable uplink-CSI statistics and selects among six standards-derived PDSCH DM-RS configurations. Rather than optimize oracle-class accuracy, we train an 11-128-64-32-6 multilayer perceptron with a utility-aware objective that preserves the throughput structure of all candidate actions. In a large-scale trajectory-disjoint evaluation covering 294 channel/speed/SNR conditions and 7,056 observations, the proposed U-SoftCE policy significantly improves throughput over a training-selected best fixed pattern by 1.30, 0.86, and 1.13 percentage points on in-distribution, CDL channel-OOD, and 300-km/h mobility-OOD splits, respectively; paired 95% bootstrap intervals exclude zero in all three cases. An independent bit-accurate Sionna NR LDPC validation over 36 held-out conditions and 50 transport blocks per candidate retains a +1.21-point gain with a 95% cluster-bootstrap interval of [0.27, 2.13]. A native C++ implementation uses 12,070 dense parameters and requires 5.59 microseconds mean inference time, while a FlexRIC-based emulated O-DU prototype demonstrates sub-10-ms closed-loop operation. These results support utility-aware objectives for compact adaptive PHY controllers while exposing the limits of exact class-accuracy optimization.
