  In the sixth-generation (6G) era, emerging large-scale computing based
applications (for example processing enormous amounts of images in real-time in
autonomous driving) tend to lead to excessive energy consumption for the 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 (RIS) have been proposed for improving the
energy efficiency by beneficially reconfiguring the wireless propagation
environment. In conventional RIS solutions, however, the received
signal-to-interference-plus-noise ratio (SINR) sometimes may even become
degraded. This is because the signals impinging upon an RIS are typically
contaminated by interfering signals which 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, termed 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 (RF) spectrum
for green 6G networks. Given the proposed framework, the RIS controller becomes
capable of intelligently `{think-and-decide}' whether to reflect or not 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.
