  Reconfigurable intelligent surface (RIS) has become a promising technology
for enhancing the reliability of wireless communications, which is capable of
reflecting the desired signals through appropriate phase shifts. However, the
intended signals that impinge upon an RIS are often mixed with interfering
signals, which are usually dynamic and unknown. In particular, the received
signal-to-interference-plus-noise ratio (SINR) may be degraded by the signals
reflected from the RISs that originate from non-intended users. To tackle this
issue, we introduce the concept of intelligent spectrum learning (ISL), which
uses an appropriately trained convolutional neural network (CNN) at the RIS
controller to help the RISs infer the interfering signals directly from the
incident signals. By capitalizing on the ISL, a distributed control algorithm
is proposed to maximize the received SINR by dynamically configuring the
active/inactive binary status of the RIS elements. Simulation results validate
the performance improvement offered by deep learning and demonstrate the
superiority of the proposed ISL-aided approach.
