mlsanityData to 7 Oct 2026

The First Pathloss Radio Map Prediction Challenge

arXiv 2310.07658 ↗First posted 11 Oct 2023eess.SP cs.LG cs.NIcited by 13

Not on the map. Why?

Abstract (arXiv)

To foster research and facilitate fair comparisons among recently proposed pathloss radio map prediction methods, we have launched the ICASSP 2023 First Pathloss Radio Map Prediction Challenge. In this short overview paper, we briefly describe the pathloss prediction problem, the provided datasets, the challenge task and the challenge evaluation methodology. Finally, we present the results of the challenge.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 2 of its 10 references that are in the map. First posted on arXiv 11 Oct 2023.

Builds on

2 of 10 references.

References · 2

  1. 2019RadioUNet: Fast Radio Map Estimation with Convolutional Neural Networkscited by 126
  2. 2022Dataset of Pathloss and ToA Radio Maps With Localization Applicationcited by 27

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 2; 2025: 4; 2026: 6.

13 papers.

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  1. 2024RadioDiff: An Effective Generative Diffusion Model for Sampling-Free Dynamic Radio Map Constructioncited by 55
  2. 2025RadioDiff-k2: Helmholtz Equation Informed Generative Diffusion Model for Multi-Path Aware Radio Map Constructioncited by 19
  3. 2023A Scalable and Generalizable Pathloss Map Predictioncited by 18
  4. 2024Radio Map Prediction from Aerial Images and Application to Coverage Optimizationcited by 10
  5. 2025RMTransformer: Accurate Radio Map Construction and Coverage Predictioncited by 5
  6. 2026RadioDiff-Flux: Efficient Radio Map Construction via Generative Denoise Diffusion Model Trajectory Midpoint Reusecited by 4
  7. 2026RadioDiff-FS: Physics-Informed Manifold Alignment in Few-Shot Diffusion Models for High-Fidelity Radio Map Constructioncited by 2
  8. 2025RadioDiff-Loc: Diffusion Model Enhanced Scattering Congnition for NLoS Localization with Sparse Radio Map Estimationcited by 0
  9. 2025Radiolunadiff: Estimation of wireless network signal strength in lunar terraincited by 0
  10. 2026Learning Coverage- and Power-Optimal Transmitter Placement from City Maps: A Comparative Study of Direct and Indirect Neural Approachescited by 0
  11. 2026EA-RMENet -- Path Loss Prediction in Urban Environments using Deep Learningcited by 0
  12. 2026RadioVIL: Anomaly-Aware Diffusion Models for Radio Map Inpainting and Zero-Shot Vehicle Localizationcited by 0
1 more
  1. 2026BeamRMX: Radiation-Pattern-Driven Learning for Generalizable Beam Radio Map Prediction and Beam Managementcited by 0