عنوان انگلیسی مقاله:
A deep reinforcement learning for user association and power control in heterogeneous networks
ترجمه فارسی عنوان مقاله:
یک یادگیری تقویتی عمیق برای ارتباط کاربر و کنترل قدرت در شبکه های ناهمگن
Sciencedirect - Elsevier - Ad Hoc Networks, 102 (2020) 102069. doi:10.1016/j.adhoc.2019.102069
Hui Ding a , Feng Zhao b , ∗, Jie Tian c , Dongyang Li a , Haixia Zhang a , d
Heterogeneous network (HetNet) is a promising solution to satisfy the unprecedented demand for higher data rate in the next generation mobile networks. Different from the traditional single-layer cellular net- works, how to provide the best service to the user equipments (UEs) under the limited resource is an urgent problem to solve. In order to efficiently address the above challenge and strive towards high network energy efficiency, the joint optimization problem of user association and power control in or- thogonal frequency division multiple access (OFDMA) based uplink HetNets is studied. Considering the non-convex and non-linear characteristics of the problem, a multi-agent deep Q-learning Network (DQN) method is studied to solve the problem. Different from the traditional methods, such as game theory, fractional programming and convex optimization, which need more and accurate network information in practice, the multi-agent DQN method requires less communication information of the environment. Moreover, for the communication environment dynamics, the maximum long-term overall network utility with a new reward function while ensuring the UE’s quality of service (QoS) requirements is achieved by using the multi-agent DQN method. Then, according to the application scenario, the action space, state space and reward function of the multi-agent DQN based framework are redefined and formulated. Sim- ulation results demonstrate that the multi-agent DQN method has the best performance on convergence and energy efficiency compared with the traditional reinforcement learning (Q-learning).
Keywords: Heterogeneous networks | User association | Power control | Reinforcement learning | Deep Q-learning network