## 2 Related Works
### Requirements
It discussed how these WI UAVs can overcome the limitations of fixed RANs by adapting to dynamic traffic demands while also covering regulatory, standardization, opportunities, and design challenges. A final interesting example is presented by the authors of [27], who developed an analytical framework to calculate the line-of-sight probability for air-to-ground channels in urban environments. It took into account the operating frequency, transmitter/receiver heights, distance, and urban obstacle characteristics to enable system-level performance evaluation of airborne platform-assisted wireless networks. Since this study operates within the domain of Industry 4.0, the aforementioned requirements must be expanded. To this end, we referred to the 3 GPP TR 22.804 standard [28], which focuses on 5G communication for automation in vertical domains covering production, goods handling, and service delivery. This context typically demands low latency, high reliability, and high availability, although other types of communication can coexist within the same 5G infrastructure. The standard also offers an overview of key communication principles for mapping industrial automation requirements onto 5G systems. Table 2 presents various control scenarios for UGVs (Automated Guided Vehicles), along with their respective latency constraints. All of them involve UGVs operating in a cyclic interaction model. Requirement P1 describes UGVs controlled by production lines or industrial robots. P2 involves the exchange of information between UGVs to enable cooperative movement. P3 describes a human remotely controlling a UGV using a camera feed. P4 addresses semi-autonomous UGVs where a human operator takes over in exceptional situations using a camera mounted on the vehicle. For our study, the P3 case was particularly relevant, as its latency requirements served as a reference point for evaluating our results. Similar research was conducted by Qualcomm, which, in [29], identified robotic motion control as a key example of an application with extreme performance requirements and highlighted how 5G can enhance efficiency, flexibility, and integration with existing and emerging industrial communication standards. All of this reflects the growing recognition within the industry of the benefits offered by wireless connectivity. For example, throughout 2024, the adoption of such solutions increased by 22%. However, Time-Sensitive Networking (TSN) solutions continue to be dominated by Industrial Ethernet, and the integration of TSN with 5G remains an active area of development [30]. This is evident in works such as [31], where the authors re \begin{table}
\begin{tabular}{c l c c} \hline \hline
**ID** & **Use Case 1** & **End-to-End** & **Message Size** \\  & & **Latency [ms]** & **[bytes]** \\ \hline P1 & Machine control & 1–10 & 40–250 \\ \hline P2 & Cooperative driving & 1–50 & 40–250 \\ \hline P3 & Video-operated remote control & 10–100 & 40–250 \\ \hline P4 & Video-operated remote control; standard mobile robot operation & 40–500 & 15 k–250 k \\  & and traffic management & & \\ \hline \hline \end{tabular} \({}^{\dagger}\) Each use case assumes mobile robots with a speed of \(\unlhd\)4 m/s and a service area of \(\unlhd\) km\({}^{2}\). Control is performed in a cyclic interaction mode.  
\end{table} 
Table 2: UGV use case (based on 3 GPP TR 22.804 [28]). viewed the integration of 5G and TSN, outlining current 3 GPP developments, architectural considerations, and remaining challenges. Similarly, in [32], the authors proposed an AI-driven approach for improved resource management and reliable communication within integrated 5G-TSN networks. In summary, 3 GPP TR 22.829 [22] helped us determine the uplink throughput targets for the LiDAR-equipped drone, while 3 GPP TR 22.804 [28] provided latency benchmarks for control message delivery in industrial drone scenarios.