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

TL;DR

Experimental results validate that the proposed FPTC-GANs method achieves the best radio map construction performance, compared with the state-of-the-art methods.

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.

Figures (4)

• Figure 1: Overall framework of the proposed FPTC-GANs method.
• Figure 2: The network architecture of the proposed RMP-GAN and RMC-GAN.
• Figure 3: Visualized radio maps (RSRP in dBm) for typical scenes.
• Figure 4: RMC-GAN performance with different numbers of measurements.