Access Control for RAN Slicing based on Federated Deep Reinforcement Learning

Network Slicing (NS) has been widely identified as a key architectural technology for SG-and-beyond systems by supporting divergent requirements sustainably. With the widespread of emerging smart devices, access control becomes an essential yet challenging issue in NS-based wireless networks due to the device-base station (BS)-NS three-layer association relationship. Meanwhile, stringent data security and device privacy concerns are increasing dramatically. In this paper, we propose an efficient access control scheme for radio access network (RAN) slicing by exploiting a federated deep reinforcement learning framework, called FDRL-AC, to improve network throughput and communication efficiency while enforcing the data security and device privacy. Specifically, we use deep reinforcement learning to train local model on devices, where horizontally federated learning (FL) is employed for parameter aggregation on BS, while vertically FL is employed for feature aggregation on the encrypted party. Numerical results show that the proposed FDRL-AC scheme can achieve significant performance gain in terms of network throughput and communication efficiency in comparison with some state-of-art solutions.