# Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces

arXiv: https://arxiv.org/abs/2103.01531 · HTML: https://mlsanity.com/paper/2103.01531/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2021.

## Built on by (most cited first, 3 of 3)

- [Pervasive Machine Learning for Smart Radio Environments Enabled by Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2205.03793/) (2022)
- [Spatiotemporal Capsule Neural Network for Vehicle Trajectory Prediction](https://mlsanity.com/paper/2303.02880/) (2023)
- [Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2112.11414/) (2021)

## Abstract (arXiv)

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.