… large-scale super-dense networks, and so on. The amazing success of deep learning in
various fields, … Hence, in this review, a pair of dominant methodologies of using DL for wireless …

Deep learning (DL) has proven its unprecedented … 6G wireless networks, new applications
and use cases have been emerging with stringent requirements for next-generation wireless …

… Moreover, they have evaluated their system using the advanced wireless network datasets.
… the deep learning methods. In addition, they are also used the standard machine learning …

… This paper proposes a wireless network traffic deep learning prediction framework LA-…
, the residual network is first used to extract the characteristics of wireless network data from …

… Deep learning improves the performance when the model-based methods fail. … deep
learning applies to wireless communication security. In this context, adversarial machine learning …

… Then, we focus on new types of deep learning networks … io by learning a universal similarity
evaluation ability utilizing a … view on the deep learning based wireless sensing field and …

… Recently, it has been shown in [20], [21] that deep neural networks … deep learning has
received significant attention [23]–[31], no prior work has considered deep learning aided wireless …

… a deep learning based end-to-end communication system for general wireless channels,
where … This problem is formulated as training a deep auto-encoder network with an untrainable …

… -hungry deep learning (DL) approaches in the context of 6G networks. Specifically, deep
unfolded … The deep unfolded approaches reviewed in this article are positioned explicitly in the …

… only covers the applications of GNNs in wireless networks, our survey has a broader …
wireless network scenarios. Then we discuss the papers focusing on a specific wireless network …