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Block Sparse Bayesian Learning-Based Channel Estimation for MIMO-OTFS Systems
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DOI：10.1109/LCOMM.2022.3144674.png)
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Abstract
En 中文
In this letter, we propose an efficient channel estimation method for multiple input multiple output orthogonal time-frequency-space systems in which each delay path cluster of the channel has multiple Dopplers. Under the channel model, the relationship between the input and output in the delay-Doppler (DD) domain is first analysed. Thereafter, based on the channel characteristics of the DD domain, we cast the channel estimation problem as a block sparse signal recovery problem, which is solved by the proposed block sparse Bayesian learning with block reorganization (BSBL-BR) method. In contrast to the traditional BSBL method, we update iteratively the size of non-sparse blocks to obtain a better channel estimation accuracy. Simulation results demonstrate the effectiveness and superiority of the proposed method over state-of-the-art methods in terms of system performance and noise robustness.
Keywords:
Channel estimation
MIMO communication
Receiving antennas
Doppler shift
Delays
Transmitting antennas
Bayes methods
MIMO-OTFS
sparse signal recovery
channel estimation
block sparse Bayesian learning
Journal
IF:
4.4
Papers:
1.3W
Citations:
2.2W