Communication-aware robot planning requires accurate predictions of wireless network performance. Current approaches rely on channel-level metrics such as received signal strength and signal-to-noise ratio, assuming these translate reliably into end-to-end throughput. We challenge this assumption through a measurement campaign in a private 5G industrial environment. We evaluate throughput predictions from a commercial ray-tracing simulator as well as data-driven Gaussian process regression models against measurements collected using a mobile robot. The study uses off-the-shelf user equipment in an underground, radio-shielded facility with detailed 3D modeling, representing a best-case scenario for prediction accuracy. The ray-tracing simulator captures the spatial structure of indoor propagation and predicts channel-level metrics with reasonable fidelity. However, it systematically over-predicts throughput, even in line-of-sight regions. The dominant error source is shown to be over-estimation of sustainable MIMO spatial layers: the simulator assumes near-uniform four-layer transmission while measurements reveal substantial adaptation between one and three layers. This mismatch inflates predicted throughput even when channel metrics appear accurate. In contrast, a Gaussian process model with a rational quadratic kernel achieves approximately two-thirds reduction in prediction error with near-zero bias by learning end-to-end throughput directly from measurements. These findings demonstrate that favorable channel conditions do not guarantee high throughput; communication-aware planners relying solely on channel-centric predictions risk overly optimistic trajectories that violate reliability requirements. Accurate throughput prediction for 5G systems requires either extensive calibration of link-layer models or data-driven approaches that capture real system behavior.

翻译：通信感知的机器人规划需要精确预测无线网络性能。当前方法依赖于信道层面的指标，如接收信号强度与信噪比，并假设这些指标能可靠地转换为端到端吞吐量。我们通过在私有5G工业环境中的测量活动对这一假设提出质疑。我们使用移动机器人采集的实测数据，评估了商业射线追踪仿真器以及数据驱动的高斯过程回归模型的吞吐量预测性能。该研究在一个具有详细三维建模的地下无线电屏蔽设施中使用现成的用户设备进行，代表了预测准确性的最佳场景。射线追踪仿真器捕捉了室内传播的空间结构，并以合理的保真度预测了信道层面的指标。然而，即使在视距区域，它也系统地高估了吞吐量。研究表明，主要的误差来源是对可持续MIMO空间层数的过高估计：仿真器假设接近均匀的四层传输，而测量结果显示实际在一到三层之间存在显著的自适应调整。这种不匹配即使在信道指标看似准确时也会导致预测吞吐量虚高。相比之下，采用有理二次核的高斯过程模型通过直接从测量数据中学习端到端吞吐量，实现了预测误差减少约三分之二且偏差近乎为零。这些发现表明，良好的信道条件并不能保证高吞吐量；仅依赖以信道为中心的预测的通信感知规划器，可能产生过于乐观的轨迹，从而违反可靠性要求。准确预测5G系统吞吐量需要对链路层模型进行广泛校准，或采用能够捕捉真实系统行为的数据驱动方法。