Low-rank and Angular Structures aided mmWave MIMO Channel Estimation with Few-bit ADCs
ID:143 View Protection:ATTENDEE Updated Time:2020-08-05 10:17:28 Hits:431 Oral Presentation

Start Time:2020-06-08 14:20(Asia/Shanghai)


Session:S Special Session » SS02Sparse And Low-Rank Signal Processing For Array Processing And Wireless Communications

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The problem of channel estimation for millimeter wave (mmWave) systems employing few-bit ADCs is studied. Since the mmWave channel is usually characterized by a geometric channel model, which is low rank and sparse in angular domains, utilizing the low-rank structure along with the sparsity improves the channel estimation performance. Specifically, this paper develops a two stage approach for mmWave channel estimation, namely, a low rank matrix recovery stage and a gridless angle recovery stage. At the first stage, because the low rank matrix undergoes a linear transform followed by a componentwise nonlinear transform, three modules named sparse Bayesian learning, linear minimum mean squared error (LMMSE) module, MMSE module are designed respectively for the signal recovery. At the second stage, utilizing the recovered low rank matrix along with the subspace, MUSIC is adopted to recover the angular information, which further improves the channel estimation performance. Numerical experiments are conducted to show the effectiveness of the proposed approach.
Jiang Zhu
Zhejiang University, China

Submission Author
Jiang Zhu Zhejiang University, China
Zhennan Liu Zhejiang University, China
Chunyi Song Zhejiang University, China
Zhiwei Xu Zhejiang University, China
Caijun Zhong Zhejiang University, China
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