2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
Login Paper Search My Schedule Paper Index Help

My ICASSP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDSS-1.5
Paper Title Meta-Learning for 6G Communication Networks with Reconfigurable Intelligent Surfaces
Authors Minchae Jung, Soonchunhyang University, South Korea; Walid Saad, Virginia Tech, United States
SessionSS-1: Beamforming for Intelligent Surfaces
LocationGather.Town
Session Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Time:Tuesday, 08 June, 13:00 - 13:45
Presentation Poster
Topic Special Sessions: Beamforming for Intelligent Surfaces
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Channel acquisition is one of the main challenges in a reconfigurable intelligent surface (RIS) system due to the passive nature of an RIS. In order to accurately estimate RIS channels, a large number of pilot symbols are required, which could yield a severe performance degradation in terms of the spectral efficiency (SE). In this paper, a practical channel acquisition and passive beamforming technique is proposed using a limited number of pilot symbols in an RIS-assisted cellular network. In particular, the proposed technique relies on a meta-learning framework. To address practical RIS challenges, the problem of maximizing the instantaneous SE is formulated and a novel approach to solve this optimization problem is developed. The proposed algorithm enables an RIS to select the optimal phase shift matrix without the need for perfect channel state information. Also, the trained parameter resulting from the proposed meta-learning algorithm can be guaranteed to converge to an optimal solution. Simulation results show a comparable performance to an exhaustive search method with a few training symbols which validates the advantages of meta-learning for an RIS system.