Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN

We propose a novel technique to make neural network robust to adversarial examples using a generative adversarial network. We alternately train both classifier and generator networks. The generator network generates an adversarial perturbation that can easily fool the classifier network by using a gradient of each image. Simultaneously, the classifier network is trained to classify correctly both original and adversarial images generated by the generator. These procedures help the classifier network to become more robust to adversarial perturbations. Furthermore, our adversarial training framework efficiently reduces overfitting and outperforms other regularization methods such as Dropout. We applied our method to supervised learning for CIFAR datasets, and experimantal results show that our method significantly lowers the generalization error of the network. To the best of our knowledge, this is the first method which uses GAN to improve supervised learning

Grouping Based Blind Interference Alignment for KK-user MISO Interference Channels

We propose a blind interference alignment (BIA) through staggered antenna switching scheme with no ideal channel assumption. Contrary to the ideal assumption that channels remain constant during BIA symbol extension period, when the coherence time of the channel is relatively short, channel coefficients may change during a given symbol extension period. To perform BIA perfectly with realistic channel assumption, we propose a grouping based supersymbol structure for $K$-user interference channels which can adjust a supersymbol length to given coherence time. It is proved that the supersymbol length could be reduced significantly by an appropriate grouping. Furthermore, it is also shown that the grouping based supersymbol achieves higher degrees of freedom than the conventional method with given coherence time.Comment: 5 pages, 3 figures, to appear in IEEE ISIT 201

Topological Interference Management with Reconfigurable Antennas

We study the symmetric degrees-of-freedom (DoF) of partially connected interference networks under linear coding strategies at transmitters without channel state information beyond topology. We assume that the receivers are equipped with reconfigurable antennas that can switch among their preset modes. In such a network setting, we characterize the class of network topologies in which half linear symmetric DoF is achievable. Moreover, we derive a general upper bound on the linear symmetric DoF for arbitrary network topologies. We also show that this upper bound is tight if the transmitters have at most two co-interferers.Comment: This work will be presented in part at the 2016 IEEE International Symposium on Information Theory (ISIT

Retrospective Interference Alignment for Two-Cell Uplink MIMO Cellular Networks with Delayed CSIT

In this paper, we propose a new retrospective interference alignment for two-cell multiple-input multiple-output (MIMO) interfering multiple access channels (IMAC) with the delayed channel state information at the transmitters (CSIT). It is shown that having delayed CSIT can strictly increase the sum-DoF compared to the case of no CSIT. The key idea is to align multiple interfering signals from adjacent cells onto a small dimensional subspace over time by fully exploiting the previously received signals as side information with outdated CSIT in a distributed manner. Remarkably, we show that the retrospective interference alignment can achieve the optimal sum-DoF in the context of two-cell two-user scenario by providing a new outer bound.Comment: 7 pages, 2 figures, to appear in IEEE ICC 201