## 6 Conclusions
However, a notable exception was observed in the more demanding LiDAR scenario at 0 dB attenuation: the smallest packet size (16 B) exhibited unexpectedly high latency, which was directly correlated with a significantly elevated downlink retransmission rate for these packets. This study clearly demonstrated that the appropriate configuration depends strongly on both the generated sensor traffic and the surrounding environment. In particular, uplink-intensive applications such as high-resolution LiDAR mapping demand careful matching of 5G parameters to system requirements. Likewise, low-latency-optimized configurations, if deployed under favorable conditions, could enable advanced industrial operations like real-time machine control or drone-based inspections. These findings emphasize the importance of not only simulation but also real-world experimentation and parameter optimization when deploying 5G for Industry 4.0. Fine-tuning system parameters to match specific use cases and conditions remains crucial for bridging the gap between the theoretical 5G potential and practical industrial performance. Looking ahead, the presented testbed methodology offers potential scalability to more complex deployment scenarios. Since the testbed utilizes real 5G modems and a wireless connection to the base station, it is well-suited for simulating multiple LiDAR-equipped UGVs. We have also collected full packet traces from the sensor, enabling realistic traffic replay. With these data and the current infrastructure, we are equipped to emulate multi-UGV scenarios, including cooperative applications, provided that an appropriate mobility model is integrated. This makes the setup a strong foundation for future research on scalability and coordination in industrial 5G deployments. An important direction for future work is improving the performance adaptability of networked systems in industrial environments. In our current testbed, parameters such as SR Period and USS candidate configurations require restarting the gNB, which limits the ability to respond to changing conditions in real time. However, dynamic adaptation is critical for maintaining high throughput and low latency when link quality varies, especially in mobile scenarios. Building on this idea, we plan to investigate adaptive control of the LiDARs output profile based on link conditions. This approach offers a practical application-layer method to sustain performance in cases where direct reconfiguration of network parameters is not possible. Conceptualization, J.K. and M.H. ; data curation, J.K. and M.H. ; investigation, J.K.; methodology, J.K. and M.H. ; project administration, J.K.; resources, M.H. ; software, J.K. and A.W. ; supervision, J.K. and M.H. ; validation, J.K. and M.H. ; visualization, J.K.; writing--original draft, J.K. and A.W. ; writing--review & editing, J.K. and M.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The raw data supporting the conclusions of this article will be made available by the authors upon request. The authors would like to thank Gdansk University of Technology Department of Computer Communications for making the PLSG laboratory available. The authors declare no conflicts of interest.