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Generative Adversarial Learning in Networking
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Generative Adversarial Network (GAN) driven deep learning approaches have gained attention due to their excellent results. Applications of GAN can be found in data augmentation, privacy preserving, anomaly detection, discriminative modeling, but for networking, they are still in nascent stages. GAN consists a generator and a discriminator connected together in an adversary mode. The generator generates new data instances and the discriminator evaluates them for authenticity. The data distributional learning capability of such generator is useful for optimization problems with dynamic and sparse data. We are exploiting these characteristics of GAN to implement intelligent networking solutions with intention to move towards autonomous networks. In particular, we are currently working on proactive mobility for 5G, where GAN is used to predict next attachment point of the user and handover instant from one attachment point to the next. The results of our model show substantial improvement comparing to state-of-the-art deep learning solutions.