Intelligent Reflecting Surface Aided Cloud Access Networks with Federated Learning

Compared with traditional cellular network architecture, cloud access network has some significant advantages in spectrum utilization, energy consumption and network construction cost. However, a high-quality forward link is required between the baseband processing unit pool and remote radio head (RRH) in the cloud access network, which results in limited RRH deployment and affects user access link transmission and coverage. To solve this issue, this paper introduces the intelligent reflector technology into the cloud access network as a solution with low energy consumption, low cost and easy deployment to deal with the existing bottlenecks. Firstly, an efficient channel information acquisition strategy based on federated learning is designed for the smart reflector to enhance the user access link, so as to achieve a compromise between the channel estimation accuracy and cost. On this basis, a robust beamforming design and optimization method of the compression mechanism of the forward link are proposed for the smart reflector to enhance the user access link and the wireless forward link, so as to improve the system transmission performance. Finally, we explore the joint resource allocation method of intelligent reflector assisted cloud access network, and improve the system energy efficiency through the collaborative configuration of intelligent reflector and cloud access network communication resources. The research of this paper will provide an important theoretical basis for the application of intelligent reflectors in cloud access networks, especially for the federated learning.