ABSTRACT
In this paper, we investigate dynamic resource selection in dense deployments of the recent 6G mobile in-X subnetworks (inXSs). We cast resource selection in inXSs as a multi-objective optimization problem involving maximization of the minimum capacity per inXS while minimizing overhead from intra-subnetwork signaling. Since inXSs are expected to be autonomous, selection decisions are made by each inXS based on its local information without signaling from other inXSs. A multi-agent Q-learning (MAQL) method based on limited sensing information (SI) is then developed, resulting in low intra-subnetwork SI signaling. We further propose a rule-based algorithm termed Q-Heuristics for performing resource selection based on similar limited information as the MAQL method. We perform simulations with a focus on joint channel and transmit power selection. The results indicate that: (1) appropriate settings of Q-learning parameters lead to fast convergence of the MAQL method even with two-level quantization of the SI, and (2) the proposed MAQL approach has significantly better performance and is more robust to sensing and switching delays than the best baseline heuristic. The proposed Q-Heuristic shows similar performance to the baseline greedy method at the 50th percentile of the per-user capacity and slightly better at lower percentiles. The Q-Heuristic method shows high robustness to sensing interval, quantization threshold and switching delay.
ABSTRACT
Evaluations of different technologies and solutions for indoor localization exist but only a few are aimed at the industrial context. In this paper, we compare and analyze two prominent solutions based on Ultra Wide Band Radio (Pozyx) and Ultrasound (GoT), both installed in an industrial manufacturing laboratory. The comparison comprises a static and a dynamic case. The static case evaluates average localization errors over 90 s intervals for 100 ground-truth points at three different heights, corresponding to different relevant objects in an industrial environment: mobile robots, pallets, forklifts and worker helmets. The average error obtained across the laboratory is similar for both systems and is between 0.3 m and 0.6 m, with higher errors for low altitudes. The dynamic case is performed with a mobile robot travelling with an average speed of 0.5 m/s at a height of 0.3 m. In this case, low frequency error components are filtered out to focus the comparison on dynamic errors. Average dynamic errors are within 0.3-0.4 m for Pozyx and within 0.1-0.2 m for GoT. Results show an acceptable accuracy required for tracking people or objects and could serve as a guideline for the least achievable accuracy when applied for mobile robotics in conjunction with other elements of a robotic navigation stack.
