# Performance of Sensor Data Process Offloading on 5G-Enabled UAVs
## 1 Introduction
This study mainly tries to demonstrate the uplink capabilities of a 5G-enabled UAV and to show the consequences of exceeding the uplink capabilities through practical experiments. From now on, the 5G-enabled UAV sensing platform, which is also a UE in the cellular network, is abbreviated as 5G-UAV. UAV sensing platforms can be equipped with a large number of sensors. Modern sensors generate a lot of information, and when combined, they can generate amounts of data large enough to surpass the uplink capacity of any modern wireless communication technology. Uplink-heavy sensing platforms, such as robots and UAVs, can typically output data that exceeds the uplink capacity of the 5G network, especially if the vast amount of spectrum in the mmWave range is not utilized. If the 5G-UAV attempts to send more data than the 5G network can handle, the consequences include queue build-up, increased packet latency, and potentially dropped packets. Applications that are regarded as closed-loop applications, such as enhanced teleoperation or autonomous UAVs that rely on external computational resources (e.g., the edge cloud), may exhibit uncertain outcomes. For example, the observed robot state is delayed in the remote endpoint, and such systems should be analyzed as a whole. Therefore, every engineer must carefully assess the capability of the current wireless communication. Then, one has to design a corresponding sensor data handling mechanism to ensure that the application stays within the bounds of the offered communication while making as much use of the available sensor data as possible [7; 10]. Here it is important to note that offloading computationally heavy processes from the UAV to the cloud has many benefits. Characteristic examples of such processes are the execution of intensive image analysis algorithms that would require extensive onboard computations that many small-scale UAVs or robots can not comprehend [10]. By offloading computationally heavy processes, less advanced onboard processors can be sufficient, which in turn may reduce the cost of the UAV. In addition, the battery life can be prolonged when less energy is needed for computations. However, offloading relies on the transmission of the sensor data to be processed to the considered cloud. Often, such sensor data transmission requires extensive communication resources and is better suited for human perception rather than computer processing [11]. Similar scenarios are present in different fields, such as smartphone cloud gaming [12] or streaming HD video applications to the cloud for extensive computer vision processing [13; 14]. Finally, specifically considering the UAV use cases, it is essential to understand that the sensor data transmission might directly affect the safe operation of a 5G-enabled UAV. For example, the offloading of optimization-based controllers responsible for the positioning of the UAV to the edge cloud, the offloading of perception or path planning algorithms, and the offloading of real-time collision avoidance algorithms to the edge cloud are some crucial examples. Section 3.3 describes such a phenomenon. The main contributions of this article are summarized as such: (1) A 5G-UAV is evaluated, with a particular emphasis on its uplink capabilities. The utilized architecture employs an edge cloud server to carry out computations for autonomous flights. (2) The 5G-UAV sensing platform and its sensor configuration, while utilizing the robotics operating system (ROS) and its sensor offloading capabilities over 5G, are discussed and evaluated. (3) Improvements in the 5G-UAV communication uplink and downlink interfaces are discussed. (4) Experimental results showing the performance when the sensor data transmitted are well within the limits of the 5G network as well as the consequences of exceeding the limits are provided for the scenarios mentioned above.