This conceptual analysis examines the dynamics of data transmission in 5G networks. It addresses various aspects of sending data from cameras and LiDARs installed on a remote-controlled ferry to a land-based control center. The range of topics includes all stages of video and LiDAR data processing from acquisition and encoding to final decoding, all aspects of their transmission and reception via the WebRTC protocol, and all possible types of network problems such as handovers or congestion that could affect the quality of experience for end-users. A series of experiments were conducted to evaluate the key aspects of the data transmission. These include simulation-based reproducible runs and real-world experiments conducted using open-source solutions we developed: "Gymir5G" - an OMNeT++-based 5G simulation and "GstWebRTCApp" - a GStreamer-based application for adaptive control of media streams over the WebRTC protocol. One of the goals of this study is to formulate the bandwidth and latency requirements for reliable real-time communication and to estimate their approximate values. This goal was achieved through simulation-based experiments involving docking maneuvers in the Bay of Kiel, Germany. The final latency for the entire data processing pipeline was also estimated during the real tests. In addition, a series of simulation-based experiments showed the impact of key WebRTC features and demonstrated the effectiveness of the WebRTC protocol, while the conducted video codec comparison showed that the hardware-accelerated H.264 codec is the best. Finally, the research addresses the topic of adaptive communication, where the traditional congestion avoidance and deep reinforcement learning approaches were analyzed. The comparison in a sandbox scenario shows that the AI-based solution outperforms the WebRTC baseline GCC algorithm in terms of data rates, latency, and packet loss.

翻译：本概念性分析研究了5G网络中数据传输的动力学特性。研究涵盖从远程控制渡轮上的摄像头与激光雷达向陆基控制中心发送数据的全过程。讨论范围包括视频与激光雷达数据从采集、编码到最终解码的全部处理阶段，通过WebRTC协议进行传输与接收的各个环节，以及可能影响终端用户体验质量的所有网络问题类型（如切换或拥塞）。通过一系列实验评估了数据传输的关键方面，包括基于仿真的可复现运行以及使用我们开发的开源解决方案进行的真实世界实验："Gymir5G"——基于OMNeT++的5G仿真系统，与"GstWebRTCApp"——基于GStreamer的WebRTC协议自适应媒体流控制应用。本研究目标之一在于制定可靠实时通信所需的带宽与延迟要求，并估算其近似值。该目标通过在德国基尔湾进行的模拟靠泊实验得以实现。实际测试中还评估了完整数据处理流程的最终延迟。此外，系列仿真实验揭示了WebRTC关键特性的影响，证明了该协议的有效性；进行的视频编解码器对比显示，硬件加速的H.264编解码器表现最佳。最后，本研究探讨了自适应通信议题，分析了传统拥塞避免方案与深度强化学习方法。沙盒场景对比表明，基于人工智能的解决方案在数据传输速率、延迟与丢包率方面均优于WebRTC基准GCC算法。