Generative Adversarial Network-Based Joint Mapping and Localization for Millimeter-Wave Communication Systems.

In this paper, we propose a novel generative adversarial network (GAN)-based joint localization and mapping (JLAM) method using angle difference of arrival (ADOA) measurements for millimeter-wave (mmWave) communication systems. The proposed method adopts a deep auto-encoder neural network as the discriminator of the GAN and models the generator as an explicit geometric ADOA function of the access point (AP) positions and the mobile terminal (MT) position, rather than as a conventional black-box neural network. By exploiting the two-dimensional distribution characteristics of high-dimensional ADOA vectors collected at a large number of random and unknown MT positions, the proposed method learns the ADOA data distribution and transforms it into the AP geometric topology. Then, the MT positions and the indoor map are estimated based on the recovered physical and virtual AP topology. The simulation results show that, under the representative setting with N=2000 measured ADOA vectors and σ=2° AOA measurement noise, the proposed method achieves an average localization error of about 0.25 m, compared with about 0.60 m for the JADE algorithm, corresponding to an error reduction of approximately 58%. The proposed method also provides more accurate room boundary estimation than JADE, confirming its effectiveness for mmWave JLAM.