Neural networks in wireless networks: Techniques, applications and guidelines

Abstract

The design of modern wireless networks, which involves decision making and parameter optimization, is quite challenging due to the highly dynamic, and often unknown, environmental conditions that characterize wireless networks. There is a common trend in modern networks to incorporate artificial intelligence (AI) techniques to cope with this design complexity. While a number of AI techniques have been profitably employed in the wireless networks community, the well-established AI framework of neural networks (NNs), well known for their remarkable generality and versatility, has been applied in a wide variety of settings in wireless networks. In particular, NNs are especially popular for tasks involving classification, learning, or optimization. In this paper, we provide both an exposition of common NN models and a comprehensive survey of the applications of NNs in wireless networks. We also identify pitfalls and challenges of implementing NNs especially when we consider alternative AI models and techniques. While various surveys on NNs exist in the literature, our paper is the first paper, to the best of our knowledge, which focuses on the applications of NNs in wireless networks.