Intelligent Cloud-Edge Collaborations Assisted Energy-Efficient Power Control in Heterogeneous Networks

• Published in: IEEE transactions on wireless communications Vol. 22; no. 11; p. 1
• Main Authors: Zhang, Lin, Peng, Jianhao, Zheng, Jiabao, Xiao, Ming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Cloud computing; cloud-edge collaboration; Collaboration; Control algorithms; Control theory; Cooperation; Deep learning; DRL; Energy efficiency; Global energy efficiency; HetNet; Optimization; Power control; Radio equipment; Real-time systems; Shadow mapping; Training; Wireless communication
• ISSN: 1536-1276; 1558-2248; 1558-2248
• DOI: 10.1109/TWC.2023.3255216

We consider a typical heterogeneous network (HetNet), which consists of a macro base station (BS) and multiple small BSs sharing the same spectrum band. Since the spectrum sharing among different BS-user links may cause severe mutual interference and degrades the global energy efficiency (GEE), it is important to optimize the transmit power of each BS and enhance the GEE. Conventional methods first collect the global instantaneous channel state information (CSI) and then optimize the transmit power in a centralized manner. Nevertheless, it is demanding to obtain the global instantaneous CSI in practical situations and the centralized optimization may easily overwhelm the coherence time of wireless channels. To tackle these issues, we leverage the strong computing capability of the (cloud) core network and the fast configuration capability of (edge) BSs and propose an intelligent cloud-edge collaboration framework. By properly designing the cloud-edge collaboration, we develop a deep reinforcement learning (DRL) based energy efficient power control algorithm. With the proposed algorithm, each BS can configure its transmit power independently and enhance the GEE. Simulation results reveal that, in both static-user and mobile-user scenarios, the proposed algorithm can provide comparable GEE performance with the conventional methods while requiring a much lower time complexity.