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Quantum mechanics denoising based channel estimation algorithm for mmWave massive MIMO systems
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AI
DOI：10.1016/j.jfranklin.2023.12.050.png)
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Abstract
En 中文
Channel estimation of millimeter wave (mmWave) massive multiple -input multiple -output (MIMO) is crucial for the application of wireless transmission. The signal system is susceptible to external noise, which reduces the accuracy of channel estimation. The denoising of the received signal is a research hotspot and challenge for channel estimation. Therefore, this paper proposes a quantum mechanics denoising-based channel estimation method. The proposed quantum mechanics denoising-based algorithm has the advantages of not relying on the original conditions, no grid error, and strong adaptive ability. The first part is that the received noisy signal is denoised. The conversion between the signal model of mmWave massive MIMO and the physical model of quantum mechanics needs to be solved. The received noisy signal is equivalent to the potential of the stationary Schrodinger equation. Then, the Hamiltonian matrix is constructed by the received signal and the corresponding eigenvalues and eigenvectors are calculated. The eigenvectors of the Hamiltonian matrix are related to the energy of Schrodinger equation, which are determined to the adaptive basis. In addition, the received signal is projected onto the adaptive basis to calculate the coefficients. The denoised received signal is reconstructed through the soft threshold processing of the coefficients. The second part is that channel estimation is performed on the denoised received signal using the l(1/2)-singular value decomposition (SVD)-based algorithm. The angle parameters are iteratively moved to the actual values by gradient descent. Besides, the initial values of the angles are obtained through the SVD preprocessing method. Simulation results show that the proposed quantum mechanics denoising-based method exhibits good estimation accuracy.
Keywords:
mmWave massive MIMO
Channel estimation
Quantum mechanics denoising
Gradient descent
SVD
Journal
J
IF:
3.7
Papers:
6.4K
Citations:
1.5W