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

TL;DR

This paper introduces a two-stage GAN-based method for radio map construction that balances accuracy and cost by predicting a primary map from environment data and refining it with sparse measurements.

Contribution

The paper proposes a novel two-stage GAN framework (RMP-GAN and RMC-GAN) incorporating self-attention and residual connections for improved radio map accuracy with reduced measurements.

Findings

FPTC-GAN achieves superior accuracy compared to state-of-the-art methods.

The method effectively balances measurement costs and construction precision.

Experimental results validate the effectiveness of the proposed approach.

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.