Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces

Bo Yang (1) , Xuelin Cao (1) , Chongwen Huang (2) , Chau Yuen (1) , L Qian (3) , Marco Di Renzo (4, 5, 6)

1 SUTD - Singapore University of Technology and Design
2 Zhejiang University
3 Prairie View A&M University
4 L2S - Laboratoire des signaux et systèmes
5 CNRS - Centre National de la Recherche Scientifique
6 Université Paris-Saclay

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-plusnoise ratio (SINR) may be degraded by the signals reflected from the RISs that originate from nonintended 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.

Mots clés: Reconfigurable intelligent surface; intelligent spectrum learning; convolutional neural network

Domaines: Electronique; Réseaux et télécommunications [cs.NI]

Bo Yang, Xuelin Cao, Chongwen Huang, Chau Yuen, L Qian, et al.. Intelligent Spectrum Learning for Wireless Networks with Reconfigurable Intelligent Surfaces. IEEE Transactions on Vehicular Technology, 2021, 70 (4), pp.3920 - 3925. ⟨10.1109/TVT.2021.3064042⟩. ⟨hal-03358217⟩