发表机构
University of California, Santa Cruz（加州大学圣克鲁兹分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出条件自适应多智能体编排器，在3GPP信道下动态选择最优估计器，相比固定策略NMSE改善最高3.6 dB，且延迟接近单估计器，实现鲁棒信道估计。
AI 中文摘要
导频辅助信道估计是正交频分复用（OFDM）接收机中的关键模块，对于5G新空口（5G-NR）和长期演进（LTE）均如此。现有大量估计器，从简单的最小二乘（LS）插值到统计最优的线性最小均方误差（LMMSE）变体，以及近年来的深度卷积去噪器，但没有任何单一估计器在所有情况下都是最优的：性能优劣取决于传播场景、参数集、工作信噪比（SNR）、移动性（多普勒）和天线配置。本文通过统一研究八种文献估计器来量化这一事实，这些估计器在基于NVIDIA Sionna生成的3GPP TR 38.901 Urban-Macro（UMa）、Urban-Micro（UMi）和Rural-Macro（RMa）信道上进行评估，涵盖5G-NR和LTE参数集，以及单输入单输出（SISO）和8×2多输入多输出（MIMO）设置。随后，我们提出了一种“条件自适应多智能体编排器”，将每个估计器视为独立智能体，并根据运行条件将任务分派给在验证集上表现最佳的智能体，无需任何“神谕”知识。该编排器对逐实现神谕的跟踪误差在1.07 dB以内，在高SNR下，相比最佳固定策略，归一化均方误差（NMSE）最多改善3.6 dB，而此时低SNR下的最优估计器不再是最优。由于智能体相互独立，并发运行它们可以以接近单一估计器的延迟实现八者中的最佳精度：数据并行分区可将墙钟时间近似缩短至1/K，其中K为工作节点数（最高6.9倍加速），而朴素的按算法分区则受限于最重智能体的阿姆达尔定律。实验结果证实，多智能体编排是跨异构5G-NR/LTE部署实现鲁棒信道估计的实用途径。
英文摘要
Pilot-aided channel estimation is a decisive block in orthogonal frequency-division multiplexing (OFDM) receivers for both 5G New Radio (5G-NR) and Long-Term Evolution (LTE). A large body of estimators exists, from simple least-squares (LS) interpolation to statistically optimal linear minimum-mean-square-error (LMMSE) variants and, more recently, deep convolutional denoisers, yet no single estimator is uniformly best: the winner depends on the propagation scenario, the numerology, the operating signal-to-noise ratio (SNR), the mobility (Doppler), and the antenna configuration. In this paper, we quantify this fact through a unified study of eight literature estimators evaluated over the 3GPP TR~38.901 Urban-Macro (UMa), Urban-Micro (UMi), and Rural-Macro (RMa) channels generated with NVIDIA Sionna, for both 5G-NR and LTE numerologies, in single-input single-output (SISO) and $8\times2$ multiple-input multiple-output (MIMO) settings. We then propose a \emph{condition-adaptive multi-agent orchestrator} that treats each estimator as an independent agent and dispatches, per operating condition, to the agent that is best on a validation split without any genie knowledge. The orchestrator tracks the per-realization oracle to within $1.07$~dB and improves the normalized mean-square error (NMSE) over the best \emph{fixed} strategy by up to $3.6$~dB at high SNR, where the low-SNR champion is no longer optimal. Because the agents are independent, running them concurrently delivers this best-of-eight accuracy at essentially single-estimator latency: a data-parallel partition scales the wall-clock nearly as $1/K$ with $K$ workers (up to $6.9\times$), whereas naive by-algorithm partitioning is Amdahl-limited by the heaviest agent. The results substantiate multi-agent orchestration as a practical route to robust channel estimation across heterogeneous 5G-NR/LTE deployments.
