Intelligent Reflective Surface Based 6G Communications for Sustainable Energy Infrastructure
Qiang Liu, Songlin Sun, Bo Rong, Michel Kadoch
Abstract
Advances in artificial intelligence (AI) techniques have offered great opportunities for the optimization of sustainable energy systems. AI techniques rely on the collection of big data, and thus it is necessary to design a fast and reliable communication network to support the need. This article studies the 6G network design based on the intelligent reflective surface (IRS) to realize an extraordinary communication platform. The IRS technology allows wireless providers to improve the RF environment by redirecting the signal to the desired location. In particular, we propose a deep reinforcement learning (DRL) method to adjust the parameters of IRS to ensure the signal quality of the 6G network. Numerical results demonstrate that our proposed IRS-based 6G network design can significantly improve the monitoring and management of sustainable energy systems.