# Why Channel-Centric Models are not Enough to Predict End-to-End Performance in Private 5G: A Measurement Campaign and Case Study

arXiv: https://arxiv.org/abs/2603.08865 · HTML: https://mlsanity.com/paper/2603.08865/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 53 references that are in the map.
- First posted on arXiv 9 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning: A Radio Map based Approach](https://mlsanity.com/paper/2009.12804/) (2020)
- [End-to-End V2X Latency Modeling and Analysis in 5G Networks](https://mlsanity.com/paper/2201.06082/) (2022)
- [Distributed Communication-aware Motion Planning for Networked Mobile Robots under Formal Specifications](https://mlsanity.com/paper/1812.01204/) (2018)
- [Toward an AI-enabled Connected Industry: AGV Communication and Sensor Measurement Datasets](https://mlsanity.com/paper/2301.03364/) (2022)
- [RCAMP: A Resilient Communication-Aware Motion Planner for Mobile Robots with Autonomous Repair of Wireless Connectivity](https://mlsanity.com/paper/1710.09303/) (2017)
- [ExPECA: An Experimental Platform for Trustworthy Edge Computing Applications](https://mlsanity.com/paper/2311.01279/) (2023)
- [MIMO in network simulators: Design, implementation and evaluation of single-user MIMO in ns-3 5G-LENA](https://mlsanity.com/paper/2404.17472/) (2024)

## Abstract (arXiv)

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.