Toward Green Network: an Expanding of Base Station Energy-Saving Algorithm in City-Scale Deployment

Green network aims to promote the sustainable development of communication systems, and base station (BS) and cells sleeping has been proven effective in reducing the power consumption of these systems. However, the current Reinforcement Learning (RL) based methods for multi-cells collaborative sleeping face significant challenges in real-world applications due to the complex users-to-cells connection relationships, and have been rarely researched in city-scale deployments. In this article, a robust RL-based multi-cells sleeping model called Graph Deep Deterministic Policy Gradient (GDDPG) is developed for handling highly complex communication scenarios. Besides, we first propose a framework for deploying multi-cells sleeping models at the city scale. Then two algorithms are put forward for determining the essential cells needed to maintain basic radio coverage and for effectively grouping these cells, which are two crucial works in the framework. Additionally, to address the temporal variation of traffic patterns, transfer learning is employed to fine-tune the pre-trained RL model periodically. Finally, we validate the feasibility of city-scale deployment algorithms and demonstrate the effectiveness of GDDPG by leveraging a computational platform and real-collected cells data from a telecom operator in China. Experimental results show that GDDPG effectively manages the sleeping states of up to 72 cells in a real-world environment. The experimental scenario is much more complex than those in other studies.