Generalizable Representation for Wireless Networks through Native Graph Topology
Summary
Uses graph representation learning and transformer architectures for wireless network optimization in 5G/6G. Award-winning solution in ITU AI/ML in 5G Challenge with 140+ competitors. Achieves real-time traffic congestion and E2E latency estimation, radio coverage optimization faster than commercial simulators, and wireless ray-tracing surrogates.
Themes
Keywords
graph neural networks, 5G, 6G, traffic prediction, ray tracing, network optimization
Poster
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