Transfer Learning for Autonomous Cell Activation Based on Relational Reinforcement Learning With Adaptive Reward

With the increasing threat of global warming due to high energy consumption of wireless network infrastructure, cell activation complements the capabilities of next-generation wireless technology. In this article, we propose an energy consumption optimization strategy based on deep reinforcement learning (DRL) and transfer learning (TL) techniques. We implement an adaptive reward to autonomously adjust parameters in a reward function to balance energy consumption and quality of service (QoS) requirement of users during the learning process. We further formulate a cell activation/deactivation problem as a Markov decision process and set up our proposed relational DRL model to meet the QoS requirements of users with a minimum number of active remote radio heads under a traffic model defined to simulate a real-world scenario. A weighted TL algorithm has been developed in DRL to validate sample data from a source task. Extensive simulations reveal that the proposed scheme based on the adaptive reward has better performance in balancing the QoS requirement of users and system energy consumption. Finally, based on our simulation results, we conclude that combining DRL with TL speeds up the learning process.