Benchmarking and establishing proper statistical validation metrics for reinforcement learning (RL) remain ongoing challenges, where no consensus has been established yet. The emergence of quantum computing and its potential applications in quantum reinforcement learning (QRL) further complicate benchmarking efforts. To enable valid performance comparisons and to streamline current research in this area, we propose a novel benchmarking methodology, which is based on a statistical estimator for sample complexity and a definition of statistical outperformance. Furthermore, considering QRL, our methodology casts doubt on some previous claims regarding its superiority. We conducted experiments on a novel benchmarking environment with flexible levels of complexity. While we still identify possible advantages, our findings are more nuanced overall. We discuss the potential limitations of these results and explore their implications for empirical research on quantum advantage in QRL.

Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisition. The concept of channel charting reduces this effort by modeling and projecting the manifold of channel state information (CSI) onto a 2D coordinate space. So far, researchers have only applied this concept to active radio localization, where a mobile device intentionally and actively emits a specific signal.In this paper, we apply channel charting to passive localization. We use a pedestrian dead reckoning (PDR) system to estimate a target's velocity and derive a distance matrix from it. We then use this matrix to learn a distance-preserving embedding in 2D space, which serves as a fingerprinting model. In our experiments, we deploy six nodes in a fully connected ultra-wideband (UWB) mesh network to show that our method achieves high localization accuracy, with an average error of just 0.24 m, even when we train and test on different targets.

The rapid growth of 5G and future 6G networks requires efficient and scalable radio access network (RAN) densification, especially in dense urban and industrial areas. Traditional planning uses manual surveys and simple propagation models, but these lack spatial accuracy and adaptability. Stochastic RF simulation tools often fail to model real-world conditions, such as material properties and geometry. This leads to poor site selection, higher costs, and rollout delays.This paper proposes an AI-based framework that combines high-resolution 3D modeling, Digital Twin technology, and deterministic ray tracing. It uses aerial and ground imagery to build detailed 3D models, enhanced with object detection and material classification through segmentation models. These models enable automatic feature extraction for RF simulation and planning. The system uses open-source 3D tools, vision transformers for segmentation, and a simulation engine with antenna radiation patterns and material-aware propagation. Tests in urban and campus settings show better prediction accuracy, less manual work, and lower costs than traditional methods. Results show that AI and Digital Twins improve and automate network deployment.

Beamforming-capable antenna arrays overcome the high free-space path loss at higher carrier frequencies. However, the beams must be properly aligned to ensure that the highest power is radiated towards (and received by) the user equipment (UE). While there are methods that improve upon an exhaustive search for optimal beams by some form of hierarchical search, they can be prone to return only locally optimal solutions with small beam gains. Other approaches address this problem by exploiting contextual information, e.g., the position of the UE or information from neighboring base stations (BS), but the burden of computing and communicating this additional information can be high. Methods based on machine learning so far suffer from the accompanying training, performance monitoring and deployment complexity that hinders their application at scale.This paper proposes a novel method for solving the initial beam-discovery problem. It is scalable, and easy to tune and to implement. Our algorithm is based on a recommender system that associates groups (i.e., UEs) and preferences (i.e., beams from a codebook) based on a training data set. Whenever a new UE needs to be served our algorithm returns the best beams in this user cluster. Our simulation results demonstrate the efficiency and robustness of our approach, not only in single BS setups but also in setups that require a coordination among several BSs. Our method consistently outperforms standard baseline algorithms in the given task.

Driven by an ever-increasing demand for higher data rates, 5G introduced communication over the millimeter-wave (mmWave) bands to fulfill this requirement. High data transmissions in this spectrum are enabled by beamforming massive MIMO antennas and the available allocated bandwidth. Of interest is the utilization of mmWave for high-accuracy positioning applications, motivated by the allocated bandwidth and beamforming characteristics of such systems. This paper provides numerical simulations on the 5G positioning reference signal reception and shows, for a real-world indoor environment, positioning performance results. The accuracy of ToA-based positioning in dependence of the beam shape and direction is determined to be at least within 6cm for LOS scenarios. We also investigate the impact of LOS path obstructions on the performance. Achieving centimeter level accuracy is subject to improvements through continuing research and refinements.