Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional (3D) wireless channel modeling, driven by advances in graphical rendering. Current approaches struggle to accurately model beyond 5G (B5G) network signaling, which often operates at higher frequencies and is more susceptible to environmental conditions and changes. Existing online learning solutions require real-time interaction with radio environment during training, which is both costly and incompatible with GPU-based processing. In response, we propose a novel approach that redefines ray trajectory generation as a sequential decision-making problem, solved with the proposed Scene-Aware Neural Decision Wireless Channel Raytracing Hierarchy (SANDWICH) approach. The SANDWICH approach leverages a decision transformer to jointly learn the optical, physical, and signal properties within each designated environment in a fully differentiable approach, which can be trained entirely on GPUs. SANDWICH offers superior performance compared to existing online learning methods, and outperforms the baseline by 4e^-2 rad in RT accuracy. Furthermore, channel gain estimation w.r.t predicted trajectory only fades 0.5 dB away from using ground truth wireless RT result for channel gain estimation.²

The sets of possible heads and tails for relations in a Knowledge Graph and a type taxonomy of participating entities are important aspects of a Knowledge Graph and constitute a large portion of a Knowledge Graph’s ontology. Making the ontology explicit helps to ensure consistency of the knowledge represented in the graph and allows for less costly maintenance and update of graph content. However, Knowledge Graphs are often conveniently formed without an explicitly described ontology, using only (head, relation, tail)-tuples. For such graphs without predefined structure, we propose learning an ontology and a hierarchical type taxonomy from the graph itself, taking advantage of the reciprocity of entity types and sets of possible heads (relational domains) and tails (relational ranges). Experiments on real-world datasets validate our approach and demonstrate the promise for leveraging machine learning methodologies to efficiently generate taxonomies and ontologies jointly for Knowledge Graphs (Our implementation can be found at https://tinyurl.com/5n6sw6yj).

Cellular coverage quality estimation has been a critical task for self-organized networks. In real-world scenarios, deep-learning-powered coverage quality estimation methods cannot scale up to large areas due to little ground truth can be provided during network design & optimization. In addition, they fall short in producing expressive embeddings to adequately capture the variations of the cells' configurations. To deal with this challenge, we formulate the task in a graph representation and so that we can apply state-of-the-art graph neural networks, that show exemplary performance. We propose a novel training framework that can both produce quality cell configuration embeddings for estimating multiple KPIs, while we show it is capable of generalising to large (area-wide) scenarios given very few labeled cells. We show that our framework yields comparable accuracy with models that have been trained using massively labeled samples.

6G will move mobile networks towards increasing levels of complexity. To deal with this complexity, optimization of network parameters is key to ensure high performance and timely adaptivity to dynamic network environments. The optimization of the antenna tilt provides a practical and cost-efficient method to improve coverage and capacity in the network. Previous methods based on Reinforcement Learning (RL) have shown effectiveness for tilt optimization by learning adaptive policies outperforming traditional tilt optimization methods. However, most existing RL methods are based on single-cell features representation, which fails to fully characterize the agent state, resulting in suboptimal performance. Also, most of such methods lack scalability and generalization ability due to state-action explosion. In this paper, we propose a Graph Attention Q-learning (GAQ) algorithm for tilt optimization. GAQ relies on a graph attention mechanism to select relevant neighbors information, improving the agent state representation, and updates the tilt control policy based on a history of observations using a Deep Q-Network (DQN). We show that GAQ efficiently captures important network information and outperforms baselines with local information by a large margin. In addition, we demonstrate its ability to generalize to network deployments of different sizes and density.

Accurate routing network status estimation is a key component in Software Defined Networking. However, existing deep-learning-based methods for modeling network routing are not able to extrapolate towards unseen feature distributions. Nor are they able to handle scaled and drifted network attributes in test sets that include open-world inputs. To deal with these challenges, we propose a novel approach for modeling network routing, using Graph Neural Networks. Our method can also be used for network-latency estimation. Supported by a domainknowledge-assisted graph formulation, our model shares a stable performance across different network sizes and configurations of routing networks, while at the same time being able to extrapolate towards unseen sizes, configurations, and user behavior. We show that our model outperforms most conventional deep-learningbased models, in terms of prediction accuracy, computational resources, inference speed, as well as ability to generalize towards open-world input.