Multi-Agent Reinforcement Learning with Common Policy for Antenna Tilt Optimization
Ericsson（爱立信）
Adriano Mendo, Jose Outes-Carnero, Yak Ng-Molina, Juan Ramiro-Moreno
备注：
7 pages and 13 figures, submitted to IAENG International Journal of Computer Science for publication consideration. The paper has been accepted with minor changes. This is the latest submitted version
This paper presents a method for optimizing wireless networks by adjusting cell parameters that affect both the performance of the cell being optimized and the surrounding cells. The method uses multiple reinforcement learning agents that share a common policy and take into account information from neighboring cells to determine the state and reward. In order to avoid impairing network performance during the initial stages of learning, agents are pre-trained in an earlier phase of offline learning. During this phase, an initial policy is obtained using feedback from a static network simulator and considering a wide variety of scenarios. Finally, agents can intelligently tune the cell parameters of a test network by suggesting small incremental changes, slowly guiding the network toward an optimal configuration. The agents propose optimal changes using the experience gained with the simulator in the pre-training phase, but they can also continue to learn from current network readings after each change. The results show how the proposed approach significantly improves the performance gains already provided by expert system-based methods when applied to remote antenna tilt optimization. The significant gains of this approach have truly been observed when compared with a similar method in which the state and reward do not incorporate information from neighboring cells.
Optimizing Reinforcement Learning Training over Digital Twin Enabled Multi-fidelity Networks
Department of Electrical and Computer Engineering, University of Miami（电子与计算机工程系，迈阿密大学）；Ericsson Research（爱立信研究）；European AI Office, European Commission（欧洲人工智能办公室，欧盟委员会）
Greedy and Transformer-Based Multi-Port Selection for Slow Fluid Antenna Multiple Access
University of A Coruña（拉科鲁尼亚大学）；CITIC（信息与通信技术研究中心）；Defense University Center at the Spanish Naval Academy（西班牙海军学院国防大学中心）；University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））
