AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems

Qiyue Li, Heng Qu, Zhi Liu, Nana Zhou, Wei Sun, Stephan Sigg, Jie Li*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

71 Citations (Scopus)


With widely deployed WiFi network and the uniqueness feature (fingerprint) of wireless channel information, fingerprinting based WiFi positioning is currently the mainstream indoor positioning method, in which fingerprint database construction is crucial. However, for accuracy, this approach requires enough data to be sampled at many reference points, which consumes excessive efforts and time. In this paper, we collect Channel State Information (CSI) data at reference points by the method of device-free localization, then we convert collected CSI data into amplitude feature maps and extend the fingerprint database using the proposed Amplitude-Feature Deep Convolutional Generative Adversarial Network (AF-DCGAN) model. The use of AF-DCGAN accelerates convergence during the training phase, and substantially increases the diversity of the CSI amplitude feature map. The extended fingerprint database both reduces the human effort involved in fingerprint database construction and the accuracy of an indoor localization system, as demonstrated in the experiments.

Original languageEnglish
Article number8891678
Pages (from-to)468-480
Number of pages13
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number3
Early online dateNov 2019
Publication statusPublished - Jun 2021
MoE publication typeA1 Journal article-refereed


  • amplitude feature
  • channel state information (CSI)
  • fingerprint
  • generative adversarial network
  • Wi-Fi positioning


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