Spectrum-Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks

In the sixth generation (6G) era, emerging large-scale computing-based applications (e.g., processing enormous amounts of images in real time in autonomous driving) tend to lead to excessive energy consumption for end users, whose devices are usually energy-constrained. In this context, energy efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize 'green' 6G networks. As a remedy, reconfigurable intelligent surfaces (RISs) have been proposed for improving energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-to-in-terference-plus-noise ratio (SINR) sometimes may become degraded. This is because the signals impinging on an RIS are typically contaminated by interfering signals that are usually dynamic and unknown. To address this issue, 'learning' the properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, referred to here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for intelligently exploiting the inherent characteristics of the radio frequency spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently 'thinking and deciding' whether or not to reflect the incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions.