## 1 Introduction  
Over the past three decades, we have witnessed a tremendous technological leap in mobile devices. Cellular networks have evolved significantly, with each generation introducing improvements not only in throughput and service quality but also in functionality, culminating in 5G's support for advanced use cases such as AR/VR, IoT, and M2M communications [1]. The continuous improvement in network capabilities has resulted in 5G being designed not only as a faster solution for traditional services like internet access, voice calls, and messaging. Already at the design stage, three distinct service types were defined by the ITU in the IMT-2020 recommendation (ITU-R M.2083-0) [2] to support emerging needs across diverse sectors: Ultra Reliable Low Latency Communications (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communications (mMTC). The authors of [3] provide a clear overview of these three service categories, describing their characteristics and applications. eMBB focuses on high data rates required for services such as ultra-high-definition (UHD) video or immersive experiences. URLLC targets use cases that demand very low latency and extremely high reliability, such as industrial automation, remote surgery, or autonomous driving. mMTC supports the deployment of massive numbers of low-power devices commonly found in smart cities or smart grid systems. The paper also discusses performance expectations, test scenarios, and traffic forecasts for each category. These three categories translate into specific demands of new services and applications, from which each is characterized by a different set of network quality parameters. Although low-latency requirements have been known for quite a long time, e.g., from increasingly popular human-robot shared control systems [4] used for remote surgeon scenarios [5], a new context of Industrial 4.0 systems points out the significant importance of two parameters simultaneously: low latency and high uplink throughput. A comprehensive overview of these applications is presented in [6], where 5G is identified as a key enabler of collaborative and autonomous robotics, remote-controlled machinery, smart production lines, and mobile inspection platforms such as UAVs and unmanned ground vehicles (UGVs). Other recent studies also highlight the importance of edge computing for local low-latency processing [7; 8], the use of private 5G networks for security and reliability [9], and the integration of enabling technologies such as AI, blockchain, and virtualization [10; 11]. To address these Industry 4.0 use cases, this study focuses on the performance of 5G in asymmetric traffic scenarios involving unmanned vehicles. Many industrial use cases require extremely low latency in the downlink and very high uplink throughput to transmit video streams, mapping data, or sensor information in real time. This is especially important for unmanned vehicles such as UAVs and UGVs used in material handling, inspection, factory automation, and surveillance. These applications often demand latency as low as 10 ms, reliability above 99.9999 percent, and uplink data rates reaching hundreds of megabits per second or more [12]. A high-level representation of such a scenario is illustrated in Figure 1, where a mobile platform equipped with a high-throughput sensor (e.g., LiDAR) communicates with a remote operator or automated control system. While the sensor generates substantial uplink traffic, downlink communication is simultaneously needed for responsive control, often via a human-machine interface such as a VR headset or dashboard. To meet these challenges, standardization bodies such as 3 GPP have proposed reference use cases and detailed performance requirements. However, most published studies focus on simulation environments, which allow for broad experimentation but fail to ac  
Figure 1: Conceptual diagram of the investigated use case: a remotely located operator controls a LiDAR-equipped unmanned ground vehicle (UGV) over a standalone 5G network.