Generalizable Representation for Wireless Networks Optimization through Native Graph Topology
Time: Tue 2025-12-16 13.15
Location: Kollegiesalen, Brinellvägen 8, Stockholm
Language: English
Subject area: Computer Science Electrical Engineering
Doctoral student: Jin Yifei , Teoretisk datalogi, TCS, Ericsson Research
Opponent: Associate Professor Zheng Chen, Division of Communication Systems, Department of Electrical Engineering, Linköping University
Supervisor: Professor Aristides Gionis, Teoretisk datalogi, TCS; Professor Sarunas Girdzijauskas, Programvaruteknik och datorsystem, SCS
QC 20251111
Abstract
Graph representation learning has become a powerful paradigm for modeling structured data, enabling machine learning systems to reason over relationships, spatial dependencies, and topological patterns. However, its potential in wireless networks remains underexplored, particularly in learning native representations of complex and dynamic wireless environments. This thesis addresses the challenge of applying graph representation learning (such as graph neural networks and transformer architectures) to wireless systems, where topology, domain heuristics, and physical constraints critically impact optimization performance and generalization.
The core problem investigated is how to construct and exploit graph representations that faithfully encode the native structure of wireless networks to enable scalable, topology-aware optimization. This includes coverage relations, interference patterns, and environment-specific propagation effects. Existing solutions in wireless machine learning often overlook these structural priors, resulting in brittle models that generalize poorly across different network deployments.
This thesis introduces a graph-centric methodology to bridge this gap. By representing wireless elements—such as base stations, links, and coverage zones as nodes and their interactions as graph edges, we develop learning architectures that integrate attention mechanisms, domain-aware features, and physics-inspired constraints. Four studies demonstrate the approach across key tasks: routing latency prediction, antenna tilt optimization, real-time radio coverage estimation, and neural ray tracing for link-level modeling.
Our results suggest that these graph-based models significantly outperform traditional baselines, achieving near-simulator accuracy with improved generalization across unseen topologies and user scenarios. They also uncover a correspondence between engineering practices and graph spectral properties, offering a new lens for understanding network design. The proposed methods reduce supervision needs and support scalable deployment across variable network configurations.
Overall, this thesis establishes graph representation learning as a foundational tool for wireless intelligence, enabling structure-informed, optimization-driven modeling across diverse network conditions. These advances pave the way towards future wireless foundation models capable of supporting a wide range of optimization, sensing, and decision-making tasks with minimal retraining.