@inproceedings{3534beb6e6fe445282a5581f83b5750d,
title = "Statistical CSI Acquisition in Multi-frequency Communication Systems",
abstract = "Multi-frequency communication is a potential strategy to overcome the limitations caused by frequency scarcity. This paper investigates the acquisition of statistical channel state information (CSI) in multi-frequency systems. We first analyze the multi-frequency channel model and reveal the relationship between spatial covariance matrices of different frequency bands. Based on the relationship, we propose a linear autoregressive (AR) method, directly establishing the mapping relationship of covariance elements between different frequency bands. In addition, with the acquired spatial covariance, we estimate APS with the maximum entropy (ME) criterion and use it to benefit downlink transmission. Simulation results verify the accuracy of the AR spatial covariance extrapolation method and show that the ME method can estimate APS with high resolution. Meanwhile, the results validate that the estimated statistical CSI can aid the realization of multi-frequency cooperative robust transmission.",
keywords = "Multi-frequency, angular power spectrum, maximum entropy, spatial covariance",
author = "Jinke Tang and Li You and Xiqi Gao and Xia, \{Xiang Gen\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 ; Conference date: 26-03-2023 Through 29-03-2023",
year = "2023",
doi = "10.1109/WCNC55385.2023.10118598",
language = "English",
series = "IEEE Wireless Communications and Networking Conference, WCNC",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE Wireless Communications and Networking Conference, WCNC 2023 - Proceedings",
address = "United States",
}