A Reinforcement Learning-Based Configuring Approach in Next-Generation Wireless Networks Using Software-Defined Metasurface

The next generation of wireless networks including Five and Six Generations (5G and 6G) can provide very high data rates as a demand for the Internet of Everything (IoE) system which connects millions of people and billions of machines. To reach such a high data rate, the wireless networks should work at high enough frequencies, such as millimeter and THz bands, which in turn suffer from a large attenuation and acute multipath fading. The idea of coating any objects in the environment with Software-Defined Metasurfaces (SDMs) was presented to control these effects by managing the electromagnetic properties of the environment. Since the programmable environment can be changed during the communication, for example, a sudden obstacle appears, this management should be adaptive. This paper presents the use of a reinforcement learning (RL) algorithm for dynamically configuring such an environment. In other words, when a change happens in the environment, for example, an obstacle blocks some EM waves, the agent receives a large punishment, and therefore a new action is selected. In our model, the transmitted electromagnetic waves and the tiles are considered as the agents and states, respectively. Moreover, the actions of each tile include absorbing or reflecting the impinging waves in a specific direction. We utilize the Q-learning technique to establish proper wireless links between the users and the access point (AP) by controlling the state of the tiles in an environment covered by the SDMs. Evaluation of the proposed model for different scenarios, including emerging sudden obstacles, indicates its potential to provide a proper signal level for all the users and improve the average received power up to 12% in comparison with the related works