AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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BeamRMX is proposed, which is the first dedicated framework to treat the spatial radiation pattern as the primary BeamRM query and learn how scene geometry transforms it into the received power field, and shows consistent gains over deterministic and diffusion baselines.
RadioDiff-v2, a dual-branch one-dimensional diffusion transformer trained with flow matching, is proposed, a dual-branch one-dimensional diffusion transformer trained with flow matching that leads every baseline on every metric.
RadioVIL is proposed, an efficient two-stage framework that reformulates joint radio map inpainting and zero-shot vehicle localization as a prior-guided physical inverse problem and unlocks accurate zero-shot vehicle localization directly from sparse radio maps, paving a robust way for ISAC at the 6G edge.
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