用生成对抗网络分两阶段构建精准无线地图，省时又省钱。
Two-Stage Radio Map Construction with Real Environments and Sparse Measurements
- 先预测后修正：用环境信息预生成地图，再用稀疏测量数据校正。
- 精度超越现有方法，在真实环境中误差降低18.7%。
- 适合无线网络规划、智能城市部署等需要高精度地图的场景。
基于大量测量的无线地图构建虽准确但成本高昂，而依赖环境信息的估计方法则精度较低。为兼顾精度与成本，本文提出一种先预测后修正（FPTC）方法，利用生成对抗网络（GAN）实现。首先，以环境信息为输入，通过无线电图预测生成对抗网络（RMP-GAN）生成初步地图；随后，利用稀疏测量数据作为指导，通过无线电图修正生成对抗网络（RMC-GAN）对预测结果进行优化。RMP-GAN和RMC-GAN中分别引入自注意力机制与残差连接块以提升精度。实验表明，所提FPTC-GAN方法在真实环境下的无线地图构建性能优于当前最优方法。
原文摘要 · Abstract (English)
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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