Channel Estimation for RIS-Aided mmWave MIMO Systems via Atomic Norm Minimization
Journal article, 2021

A reconfigurable intelligent surface (RIS) can shape the radio propagation environment by virtue of changing the impinging electromagnetic waves towards any desired directions, thus, breaking the general Snell’s reflection law. However, the optimal control of the RIS requires perfect channel state information (CSI) of the individual channels that link the base station (BS) and the mobile station (MS) to each other via the RIS. Thereby super-resolution channel (parameter) estimation needs to be efficiently conducted at the BS or MS with CSI feedback to the RIS controller. In this paper, we adopt a two-stage channel estimation scheme for RIS-aided millimeter wave (mmWave) MIMO systems without a direct BS-MS channel, using atomic norm minimization to sequentially estimate the channel parameters, i.e., angular parameters, angle differences, and the products of propagation path gains. We evaluate the mean square error of the parameter estimates, the RIS gains, the average effective spectrum efficiency bound, and average squared distance between the designed beamforming and combining vectors and the optimal ones. The results demonstrate that the proposed scheme achieves super-resolution estimation compared to the existing benchmark schemes, thus offering promising performance in the subsequent data transmission phase.

Atomic norm minimization

channel parameter estimation

millimeter wave MIMO

compressive sensing

reconfigurable intelligent surface


Jiguang He

University of Oulu

Henk Wymeersch

Chalmers, Electrical Engineering, Communication, Antennas and Optical Networks

Markku Juntti

University of Oulu

IEEE Transactions on Wireless Communications

15361276 (ISSN) 15582248 (eISSN)

Vol. 20 9 5786-5797 9398559

Multi-dimensional Signal Processing with Frequency Comb Transceivers

Swedish Research Council (VR) (2018-03701), 2018-12-01 -- 2021-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories


Communication Systems

Signal Processing



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