Device association for RAN slicing based on hybrid federated deep reinforcement learning

Network slicing (NS) has been widely identified as a key architectural technology for 5G-and-beyond systems by supporting divergent requirements in a sustainable way. In radio access network (RAN) slicing, due to the device-base station (BS)-NS three layer association relationship, device association (including access control and handoff management) becomes an essential yet challenging issue. With the increasing concerns on stringent data security and device privacy, exploiting local resources to solve device association problem while enforcing data security and device privacy becomes attractive. Fortunately, recently emerging federated learning (FL), a distributed learning paradigm with data protection, provides an effective tool to address this type of issues in mobile networks. In this paper, we propose an efficient device association scheme for RAN slicing by exploiting a hybrid FL reinforcement learning (HDRL) framework, with the aim to improve network throughput while reducing handoff cost. In our proposed framework, individual smart devices train a local machine learning model based on local data and then send the model features to the serving BS/encrypted party for aggregation, so as to efficiently reduce bandwidth consumption for learning while enforcing data privacy. Specifically, we use deep reinforcement learning to train the local model on smart devices under a hybrid FL framework, where horizontal FL is employed for parameter aggregation on BS, while vertical FL is employed for NS/BS pair selection aggregation on the encrypted party. Numerical results show that the proposed HDRL scheme can achieve significant performance gain in terms of network throughput and communication efficiency incomparison with some state-of-the-art solutions

Dynamic Handler Framework for Network Slices Management

Flexibility, scalability and programmability are the main goals of future network design. To meet these goals, the network has to ensure a complete management and control of all network resources. For instance, it has to react to the fluctuation of the load and change the assignment of network resources if needed. The key problem is how and when to add and remove resources in order to meet the required Quality of Service (QoS) of users. In this paper, we consider a network based on Software Defined Networking (SDN), Network Function Virtualization (NFV) and Network Slicing technologies. We interest on the management of the deployed network slices. To this end, we propose a Dynamic Handler Framework (DHF) that collects information about slice performances and then determines if the slice resources need to be modified or not. The proposed framework is based on a fuzzy logic algorithm that selects the adequate management decision based on two criteria which are the load ratio and the predicted load-time fairness index. In this paper, a mathematical formulation of our proposal and an evaluation of its efficiency are presented