Training Signal Design for Sparse Channel Estimation in Intelligent Reflecting Surface-Assisted Millimeter-Wave Communication
- Authors
- Noh, S.; Yu, H.; Sung, Y.
- Issue Date
- 4월-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Bayes methods; Channel estimation; Sparse matrices; Training; Transmission line matrix methods; Transmitters; Wireless communication
- Citation
- IEEE Transactions on Wireless Communications, v.21, no.4, pp.2399 - 2413
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Wireless Communications
- Volume
- 21
- Number
- 4
- Start Page
- 2399
- End Page
- 2413
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/140210
- DOI
- 10.1109/TWC.2021.3112173
- ISSN
- 1536-1276
- Abstract
- In this paper, the problem of training signal design for intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) communication under a sparse channel model is considered. The problem is approached based on the Cramér-Rao lower bound (CRB) on the mean-square error (MSE) of channel estimation. By exploiting the sparse structure of mmWave channels, the CRB for the channel parameter composed of path gains and path angles is derived in closed form under Bayesian and hybrid parameter assumptions. Based on the derivation and analysis, an IRS reflection pattern design method is proposed by minimizing the CRB as a function of design variables under constant modulus constraint on reflection coefficients. Extensions of the proposed design to a multi-antenna transceiver, a uniform planar array (UPA)-based IRS, and multi-user case are discussed. Numerical results validate the effectiveness of the proposed design method for sparse mmWave channel estimation. IEEE
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