Two-Stage Radio Map Construction with Real Environments and Sparse Measurements

• URL: http://arxiv.org/abs/2410.18092v1
• Date: Tue, 08 Oct 2024 09:15:27 GMT
• Title: Two-Stage Radio Map Construction with Real Environments and Sparse Measurements
• Authors: Yifan Wang, Shu Sun, Na Liu, Lianming Xu, Li Wang,
• Abstract summary: A first-predict-then-correct (FPTC) method is proposed by leveraging generative adversarial networks (GANs) A primary radio map is first predicted by a radio map prediction GAN (RMP-GAN) taking environmental information as input. Then, the prediction result is corrected by a radio map correction GAN (RMC-GAN) with sparse measurements as guidelines. Experimental results validate that the proposed FPTC-GANs method achieves the best radio map construction performance.
• Score: 11.432502140838867
• License:
• Abstract: Radio map construction based on extensive measurements is accurate but expensive and time-consuming, while environment-aware radio map estimation reduces the costs at the expense of low accuracy. Considering accuracy and costs, a first-predict-then-correct (FPTC) method is proposed by leveraging generative adversarial networks (GANs). A primary radio map is first predicted by a radio map prediction GAN (RMP-GAN) taking environmental information as input. Then, the prediction result is corrected by a radio map correction GAN (RMC-GAN) with sparse measurements as guidelines. Specifically, the self-attention mechanism and residual-connection blocks are introduced to RMP-GAN and RMC-GAN to improve the accuracy, respectively. Experimental results validate that the proposed FPTC-GANs method achieves the best radio map construction performance, compared with the state-of-the-art methods.

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