Deep Learning for Wireless Networking: The Next Frontier
Yu Cheng, Bo Yin, Shuai Zhang · IEEE Wireless Communications · 2021
With the growth of mobile technology in the last decade, wireless networks have become an integral part of our everyday lives. To meet the increasingly stringent application requirements, more and more network resources and features are becoming available, which requires innovative system designs such that the configuration and management of the networks can be performed automatically and autonomously. Due to its superior capability of discovering insightful knowledge in a data-driven manner, the emerging deep learning (DL) technology has shown great potential to fulfil this goal. This article systematically reviews recent efforts in leveraging DL for addressing wireless network optimization problems, presenting a fundamental understanding of where and how the supremacy of DL based approaches comes versus the conventional modeling based approaches. The basic research challenges and some promising research directions for fully exploiting the potential of DL in wireless network optimization are also discussed. The effectiveness of DL is illustrated with an innovative case study of integrating DL with multi-hop wireless network flow optimization.