## 5 Results and Discussion
### Summary
Furthermore, in the more demanding scenario, the same configuration maintained end-to-end latency values below the P1 threshold up to 20 dB attenuation. This demonstrates remarkable robustness even under challengingconditions and confirms that, under favorable circumstances, the most stringent P1 latency requirement is achievable for both less and more demanding sensor applications. A clear trend observed across all scenarios is that larger packet sizes generally lead to higher latency, especially under increased attenuation. This behavior reflects the expected impact of packetization and transmission scheduling in wireless networks, where larger packets are more susceptible to queuing delays and retransmissions. However, we also observed an interesting exception in the more demanding LiDAR scenario: for the smallest packet size (16 B) at 0 dB attenuation, the average latency was unexpectedly high, accompanied by a significantly increased downlink retransmission rate. It is also worth emphasizing that we selected two specific LiDAR parameter sets. However, alternative configurations could be selected depending on specific needs. Suppose that the uplink traffic generated would require around 85 Mbps, similar to the scenario with the less demanding sensor configuration. If the Dual Return mode was not necessary and the priority was resolution and a full field of view, one could select the Low Data Rate mode with a 360-degree field of view, a resolution of 2048, and 10 frames per second. Similarly, if a higher frame rate was preferred over resolution, instead of using the Dual Return mode with 10 frames per second and a resolution of 1024, one could opt for the Dual Return mode with a 512 resolution and 20 frames per second. Given the above results, we are not limited to the LiDAR-based scenario. The same conclusions can apply to other use cases defined in Table 1, such as 8K video transmission, or any application requiring high uplink throughput and appropriately low latency. Very important conclusions can be drawn regarding the parameters that contributed to the observed performance. Bandwidth is certainly critical for achieving high throughput. In our case, the higher value of 60 MHz proved to be more effective. However, the combination of Auto CSI, TRS (Tracking Reference Signal), and SSB (Synchronization Signal Block) was present in both top-performing configurations in the Stage II scenarios. Notably, under the more demanding sensor configuration, these mechanisms provided the best overall performance in terms of both throughput and latency, regardless of environmental conditions, which is crucial for mobile drones. Auto CSI, understood here as the automatic selection of the optimal channel state information configuration at the initial stage of communication, enables the efficient use of available MIMO modes and enhances precoding, as described in [55]. This feature supports both low- and high-resolution CSI feedback, allowing the system to match the most effective feedback type to the current spatial correlation of the channel, which leads to measurable throughput gains in realistic 5G-NR scenarios. The inclusion of TRS, leveraging the NZP CSI-RS mechanism, further improves channel estimation accuracy and supports reliable link adaptation. Recent studies demonstrate that NZP CSI-RS-based interference measurement, as standardized in 3 GPP TS 38.214, enables a more precise assessment of interference and adaptation of transmission parameters, such as modulation and coding rate, to current radio conditions. This process, often managed through a Channel Quality Indicator (CQI) reported by the user device, directly results in increased throughput and improved service quality in interference-limited or cell-edge scenarios [56; 57]. SSB plays a key role in ensuring robust synchronization and initial access.