TY - JOUR TI - Deep reinforcement learning for resource management in network slicing AU - Li, Rongpeng AU - Zhao, Zhifeng AU - Sun, Qi AU - I, Chih-Lin AU - Yang, Chenyang AU - Chen, Xianfu AU - Zhao, Mingjian AU - Zhang, Honggang T2 - IEEE Access AB - Network slicing is born as an emerging business to operators by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users’ activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective. CN - 3 DA - 2018/11/19/ PY - 2018 DP - 3.9 VL - 6 SP - 74429 EP - 74441 SN - 2169-3536 UR - https://www.rongpeng.info/files/Paper_Access2018_DQN_Slicing.pdf ER -