## 5 Results and Discussion
### Summary  
The previous sections presented the experimental results obtained under various LiDAR and base station configurations. Here, we interpret those results and discuss the key findings, focusing on aspects such as packet loss, environmental influence, packet size sensitivity, configuration flexibility, and the impact of critical parameters. The selection of an appropriate configuration is primarily determined by the traffic characteristics of the sensor. This relationship becomes evident when comparing Table 5 with Table 6 and Figure 7. Table 5 demonstrates that latency-optimized configurations can achieve up to three times lower latency under idle uplink conditions compared to throughput-optimized alternatives. These configurations satisfy not only the P3 but also the P2 requirements and, in some cases, approach the P1 threshold, which is particularly relevant given that the standard refers to end-to-end latency, whereas our tests were based on RTT. They are also capable of transmitting the full sensor traffic while meeting the S2 requirement. However, Table 6 at 0 dB of additional attenuation and Figure 7, which involves simultaneous sensor data transmission, indicate that even under moderate traffic levels, well below the theoretical capacities of configurations 5 and 6, latency increased nearly threefold and over sevenfold, respectively. In contrast, configurations 1 to 4, which initially exhibited higher latency, showed only moderate degradation (up to 35%) or stable performance. This behavior is attributable to link saturation effects, which result in packet delays and losses. While similar behavior was expected for the more demanding LiDAR setup due to traffic exceeding available throughput, it was not anticipated in less demanding scenarios. This underscores the importance of conducting scenario-specific performance testing prior to deployment. Another important factor influencing performance was the surrounding radio environment. While the testbed was deployed in a controlled indoor laboratory, we used a programmable attenuator to emulate changing conditions, such as increased signal loss caused by distance or obstacles. This setup enabled repeatable and fine-grained evaluation of how different configurations responded to varying attenuation levels. Across both LiDAR scenarios, a particularly interesting observation was made at the 10 dB attenuation level. Despite the degradation in link conditions, several configurations, especially those optimized for high throughput, maintained stable performance. This effect is partially attributable to mechanisms such as Uplink Power Control, which dynamically adjusts transmission power in response to link degradation. As a result, configurations that might otherwise suffer from increased latency or reduced throughput were able to compensate for moderate signal losses. Regarding the scenario that was of primary interest to us, namely, the combined industrial use case specified by 3 GPP, which requires an uplink throughput of at least 120 Mbps (S2) and an end-to-end latency below 100 ms (P3), the best-performing setup was the configuration using automatic CSI, TRS, and SSB. This configuration maintained acceptable performance even under increased attenuation levels. Notably, it was also capable of supporting more demanding applications, such as cooperative driving, provided the radio conditions did not degrade beyond moderate thresholds. In controlled environments, this setup demonstrated RTT values low enough that, based on direct end-to-end latency measurements, certain configurations nearly met the P1 requirement outlined in Table 2 even under 30 dB attenuation. Importantly, for the best-performing configuration, end-to-end latency analysis confirmed that the strict P1 latency constraint (below 10 ms) was consistently satisfied for all tested packet sizes and attenuation levels in the less demanding LiDAR scenario.