Method of Mobile Speed Measurement Using Semi-Supervised Masked Auxiliary Classifier Generative Adversarial Networks

We propose a semi-supervised masked auxiliary classifier generative adversarial network (SM-ACGAN) that has good classification performance in situations where labeled training data are limited. To develop SM-ACGAN, we combine the strengths of SSGAN (semi-supervised GAN), ACGAN-SG (auxiliary classifier GAN based on spectral normalization and gradient penalty), and MaskedGAN. Additionally, we devise a novel masking technique that performs masking adaptively depending on the real/fake ratio of the input data and a novel regularization technique that stabilizes the generator training depending on the maximum ratio of the average power of the generated fake data to the average power of the noise latent variables. Finally, we devise a rule of selecting an appropriate quantity of unlabeled data and labeled fake data generated by the generator for effective data augmentation. Through simulations, we demonstrate that SM-ACGAN has lower root mean square error (RMSE) values and lower variance, demonstrating superior mobile speed measurement performance on Rician channels compared to ACGAN-SG, MaskedGAN, SSGAN, a CNN (convolutional neural network), and a DNN (deep neural network).