… Leveraging on that, we propose a topology-aware deep learning (TADL) framework as
illustrated in fig. 1 for wireless network flow optimization. This is to tackle the aforementioned …

… future wireless networking applications … deep learning in the development of wireless
communications. We highlight the intuitions and key technologies of deep learning-driven wireless …

… Motivated by the success of deep learning, in this work we propose and present an integration
of wireless multimedia systems and deep learning. We start with decomposing a wireless …

… to realize the recognition of specific wireless technologies. Meanwhile, some emerging …
in mobile communication network [11]. In this paper, the need of deep learning in signal …

… techniques, deep learning (DL) frameworks have been recently investigated in wireless
resource … In particular, “learning to optimize” approaches in [7]–[13] have received considerable …

… In this work, we present the cooperative resource allocation using deep learning in 5G based
wireless networks. The contribution and the novelty of this work are summarized as follows: …

… in sensor networks. In this paper, we design deep learning models to address the wireless
… Specifically, we design an encoder-decoder based sequence-to-sequence deep learning …

… we propose a deep learning approach for realizing DFLAR. Specifically, we design a sparse
autoencoder network to automatically learn discriminative features from the wireless signals …

… In this paper, an IDS for wireless network traffic coupled to a wrapper based feature extraction
technique was proposed. The IDS was built using Feed Forward Deep Neural Networks …

… scale wireless networks, where "large scale" refers to the high population density of network
endpoints (or nodes) and the extensive coverage area. Furthermore, wireless networks are …