Deep learning-based optimal ris interaction exploiting previously sampled channel correlations

Creative Commons License

Aygül M. A., Nazzal M., ARSLAN H.

2021 IEEE Wireless Communications and Networking Conference, WCNC 2021, Nanjing, China, 29 March - 01 April 2021, vol.2021-March identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 2021-March
  • Doi Number: 10.1109/wcnc49053.2021.9417591
  • City: Nanjing
  • Country: China
  • Keywords: Deep learning, massive MIMO, phase optimization, previous channel information, reconfigurable intelligent surface
  • Istanbul Medipol University Affiliated: Yes


The reconfigurable intelligent surface (RIS) technology has attracted interest due to its promising coverage and spectral efficiency features. However, some challenges need to be addressed to realize this technology in practice. One of the main challenges is the configuration of reflecting coefficients without the need for beam training overhead or massive channel estimation. Earlier works used estimated channel information with deep learning algorithms to design RIS reflection matrices. Although these works can reduce the beam training overhead, still they overlook existing correlations in the previously sampled channels. In this paper, different from existing works, we propose to exploit the correlation in the previously sampled channels to estimate RIS interaction more reliably. We use a deep multilayer perceptron for this purpose. Simulation results reveal performance improvements achieved by the proposed algorithm.