RME-GAN: A Learning Framework for Radio Map Estimation based on Conditional Generative Adversarial Network

2022/12/24 by Songyang Zhang, Zhang, Songyang, Achintha Wijesinghe +3 · 18 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Animal Vocal Communication and Behavior #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Signal Processing (eess.SP) #Speech and Audio Processing #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2212.12817

openalex publication_date 2022/12/24 · openalex created_date 2023/01/04 · openalex updated_date 2026/07/28

Abstract

Outdoor radio map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) and cellular systems. Radio map describes spatial signal strength distribution and provides network coverage information. A practical goal is to estimate fine-resolution radio maps from sparse radio strength measurements. However, non-uniformly positioned measurements and access obstacles can make it difficult for accurate radio map estimation (RME) and spectrum planning in many outdoor environments. In this work, we develop a two-phase learning framework for radio map estimation by integrating radio propagation model and designing a conditional generative adversarial network (cGAN). We first explore global information to extract the radio propagation patterns. We then focus on the local features to estimate the effect of shadowing on radio maps in order to train and optimize the cGAN. Our experimental results demonstrate the efficacy of the proposed framework for radio map estimation based on generative models from sparse observations in outdoor scenarios.

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Related

• TiRE-GAN: Task-Incentivized Generative Learning for Radiomap Estimation
• Two-Stage Radio Map Construction with Real Environments and Sparse Measurements
• Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN
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