Channel estimation for RIS-aided mmWave MIMO systems via atomic norm minimization
|Author:||He, Jiguang1; Wymeersch, Henk2; Juntti, Markku1|
1Centre forWireless Communications, FI-90014, University of Oulu, Finland
2Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden
|Online Access:||PDF Full Text (PDF, 2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021041410364
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2021-04-14
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.
IEEE transactions on wireless communications
|Pages:||1 - 12|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work is supported by Horizon 2020, European Union’s Framework Programme for Research and Innovation, under grant agreement no. 871464 (ARIADNE). This work is also partially supported by the Academy of Finland 6Genesis Flagship (grant 318927) and Swedish Research Council (grant no. 2018-03701).
|EU Grant Number:||
(871464) ARIADNE - Artificial Intelligence Aided D-band Network for 5G Long Term Evolution
|Academy of Finland Grant Number:||
318927 (Academy of Finland Funding decision)
© The Authors 2021. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.