A Survey on Deep Learning for Cloud Radio Access Networks
Rehenuma Tasnim Rodoshi, Seokjoo Shin, Wooyeol Choi · 2020
With tremendous usage of mobile applications, the demand for high speed and low latency connections of the huge number of mobile users is increasing. Cloud radio access network (C-RAN) is a potential mobile network architecture for the next-generation wireless communication, which can meet the requirements of massively increasing data traffic and user demand. In C-RAN, the data processing unit can be centralized and virtualized in data centers and can be shared among distributed base stations. Deep learning (DL), with the recent breakthrough, appears to be a viable approach for facilitating the data processing capability, resource management in the cloud, and predicting traffic in cellular communication. The convergence of C-RAN and DL is believed to bring new possibilities to both interdisciplinary researches and industrial applications. This article provides a comprehensive survey of the state-of-the-art DL techniques applied in C-RAN. A brief introduction is given in the C-RAN architecture and DL techniques to have insights on these two emerging technologies. The reviewed works are categorized in terms of their optimization objectives mentioning the key ideas of DL applied in the works. Research challenges and open research issues are also highlighted to provide future research direction.