A flow-matching 1D diffusion transformer predicts angular radio maps from building geometry, achieving distributional fidelity, per-bin accuracy, beam selection, and Bayesian localization from a single zero-shot model across 99 environments.
Large language models for automated machine learning
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abstract
Over the last years, several works have explored the application of deep learning algorithms to determine the large-scale signal fading (also referred to as ``path loss'') between transmitter and receiver pairs in urban communication networks. The central idea is to replace costly measurement campaigns, inaccurate statistical models or computationally expensive ray-tracing simulations by machine learning models which, once trained, produce accurate predictions almost instantly. Although the topic has attracted attention from many researchers, there are few open benchmark datasets and codebases that would allow everyone to test and compare the developed methods and algorithms. We take a step towards filling this gap by releasing a publicly available dataset of simulated path loss radio maps together with realistic city maps from real-world locations and aerial images from open datasources. Initial experiments regarding model architectures, input feature design and estimation of radio maps from aerial images are presented and the code is made available.
years
2026 2representative citing papers
Spatial Atlas implements compute-grounded reasoning via a structured scene graph engine and deterministic computations to deliver competitive accuracy on spatial QA and Kaggle ML benchmarks while preserving interpretability.
citing papers explorer
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RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization
A flow-matching 1D diffusion transformer predicts angular radio maps from building geometry, achieving distributional fidelity, per-bin accuracy, beam selection, and Bayesian localization from a single zero-shot model across 99 environments.
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Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks
Spatial Atlas implements compute-grounded reasoning via a structured scene graph engine and deterministic computations to deliver competitive accuracy on spatial QA and Kaggle ML benchmarks while preserving interpretability.