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Joint Sparsity Pattern Learning Based Channel Estimation for Massive MIMO-OTFS Systems
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DOI：10.1109/TVT.2024.3375027.png)
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摘要
En 中文
We propose a channel estimation scheme based on joint sparsity pattern learning (JSPL) for massive multi-input multi-output (MIMO) orthogonal time-frequency-space (OTFS) modulation aided systems. By exploiting the potential joint sparsity of the delay-Doppler-angle (DDA) domain channel, the channel estimation problem is transformed into a sparse recovery problem. To solve it, we first apply the spike and slab prior model to iteratively estimate the support set of the channel matrix, and a higher-accuracy parameter update rule relying on the identified support set is introduced into the iteration. Then the specific values of the channel elements corresponding to the support set are estimated by the orthogonal matching pursuit (OMP) method. Both our simulation results and analysis demonstrate that the proposed JSPL channel estimation scheme achieves an improved performance over the representative state-of-the-art baseline schemes, despite its reduced pilot overhead.
Keyword:
Channel estimation
OFDM
Symbols
Matching pursuit algorithms
Estimation
Channel models
Massive MIMO
Bayesian learning
channel estimation
joint sparsity
massive MIMO
OTFS
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
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