## 6 Conclusions  
This study presented a comprehensive evaluation of 5G performance in an industrial use case involving a LiDAR-equipped unmanned ground vehicle (UGV). Using a real-world testbed and commercial hardware, we assessed how different base station configurations influence both uplink throughput and downlink latency under various radio conditions. The testing process was divided into two stages. First, we identified configurations with the greatest potential, and then we evaluated the selected ones more thoroughly using different attenuation levels and packet sizes. The results show that configurations using automatic Channel State Information (CSI), Tracking Reference Signal (TRS), and Synchronization Signal Block (SSB) provided the most robust and consistent performance. Automatic CSI enabled the selection of the optimal feedback configuration at the initial stage, ensuring the efficient use of MIMO modes and enhancing precoding. TRS, implemented using NZP CSI-RS, improved the accuracy of channel estimation and supported reliable link adaptation. SSB ensured precise synchronization and initial access, leading to higher signal-to-noise ratios, lower bit error rates, and fewer retransmissions. Together, these mechanisms helped maintain high throughput and minimize latency, even under degraded radio conditions. We also found that tuning the Scheduling Request (SR) period and increasing the number of PDCCH candidates at Aggregation Level 2 (AL2) from 4 to 8 significantly improved uplink reliability and latency, particularly under degraded radio conditions. This highlights the importance of fine-grained control over uplink signaling parameters in achieving industrial-grade performance. It is worth noting that the results do not show a clear or consistent relationship between packet size and latency. While it might seem intuitive that larger packets cause more delay, our measurements suggest more complex behavior. This may be influenced by factors such as grant timing, scheduling delays, and auto-configuration mechanisms like Auto CSI, TRS, and SSB. However, these are only some of the possible contributors. Other elements of the scheduling process or resource management may also play a role, and not all of them are exposed or well understood. Therefore, we caution against assuming any fixed trend based solely on the packet size. For scenarios that require uplink throughput above 120 Mbps (S2) and control latency below 100 milliseconds (P3), such as remotely piloted drones with video feedback, several configurations met the requirements even under added attenuation. However, more demanding applications like cooperative driving typically require latency between 1 and 50 milliseconds (P2). Only a few configurations met these tighter constraints when signal quality was reduced. Notably, in well-controlled radio environments, certain configurations achieved latency performance nearly low enough to support even stricter use cases, such as machine control over UGVs, where end-to-end latency must remain within the 1 to 10 ms (P1) range. A particularly noteworthy finding is that, under controlled conditions, several configurations achieved end-to-end latency well within the strictest P1 requirement (below 10 ms), and this was maintained across all tested packet sizes and most attenuation levels. This demonstrates the suitability of these solutions for the most demanding industrial use cases, such as real-time robot or vehicle control. Across both less and more demanding LiDAR scenarios, we observed the expected trend that increasing the packet size tends to result in higher average latency, especially as radio conditions worsen. This relationship was especially pronounced at high attenuation, where both the average delay and its variability increased substantially for the largest packets.