Lutz Ewe

Qi Liao, Tianlun Hu, Nikolaj Marchenko et al.

To support Industry 4.0 applications with haptics and human-machine interaction, 6G requires a new framework that is fully autonomous, visual, and interactive. In this paper, we provide an end-to-end solution, HARU, for private network planning services, especially industrial networks. The solution consists of the following functions: collecting visual and sensory data from the user device, reconstructing 3D radio propagation environment and conducting network planning on a server, and visualizing network performance with AR on the user device with enabled haptic feedback. The functions are empowered by three key technical components: 1) vision- and sensor fusion-based 3D environment reconstruction, 2) ray tracing-based radio map generation and network planning, and 3) AR-assisted network visualization enabled by real-time camera relocalization. We conducted the proof-of-concept in a Bosch plant in Germany and showed good network coverage of the optimized antenna location, as well as high accuracy in both environment reconstruction and camera relocalization. We also achieved real-time AR-supported network monitoring with an end-to-end latency of about $32$ ms per frame.

Cristian Tatino, Nikolaos Pappas, Ilaria Malanchini et al.

Multi-connectivity is emerging as a promising solution to provide reliable communications and seamless connectivity for the millimeter-wave frequency range. Due to the blockage sensitivity at such high frequencies, connectivity with multiple cells can drastically increase the network performance in terms of throughput and reliability. However, an inefficient link scheduling, i.e., over and under-provisioning of connections, can lead either to high interference and energy consumption or to unsatisfied user's quality of service (QoS) requirements. In this work, we present a learning-based solution that is able to learn and then to predict the optimal link scheduling to satisfy users' QoS requirements while avoiding communication interruptions. Moreover, we compare the proposed approach with two base line methods and the genie-aided link scheduling that assumes perfect channel knowledge. We show that the learning-based solution approaches the optimum and outperforms the base line methods.