Abstract
The imperfection of antenna array degrades the communication performance in the millimeter wave (mmWave) communication system. In this paper, the problem of channel estimation for the mmWave communication system is investigated, and the unknown mutual coupling (MC) effect between antennas is considered. By exploiting the channel sparsity in the spatial domain with mmWave frequency bands, the problem of channel estimation is converted into that of sparse reconstruction. The MC effect is described by a symmetric Toeplitz matrix, and the sparse-based mmWave system model with MC coefficients is formulated. Then, a two-stage method is proposed by estimating the sparse signals and MC coefficients iteratively. Simulation results show that the proposed method can significantly improve the channel estimation performance in the scenario with unknown MC effect and the estimation performance for both direction of arrival (DOA) and direction of departure (DoD) can be improved by about 8 dB by reducing the MC effect about 4 dB.
Highlights
Millimeter wave communication with the frequency bands of 30–300 GHz will be a promising technology in the 5G cellular networks [1,2,3]
In [11], a joint sparse and low-rank structure is exploited, and a two-stage compressed sensing (CS) method has been proposed for the mmWave channel estimation; the approximate message passing (AMP) method has been extended by the nearest neighbor pattern learning algorithm to improve the attainable channel estimation performance in [12]; a channel estimation algorithm based on the alternating direction method of multipliers has been given in [7]
The direction of departure (DoD)/direction of arrival (DoA) estimation performance is measured by root-mean-square error (RMSE)
Summary
Millimeter wave (mmWave) communication with the frequency bands of 30–300 GHz will be a promising technology in the 5G cellular networks [1,2,3]. To improve the performance of mmWave communication, the channel estimation methods are essential to obtain the associated channel parameters including the direction of arrival (DoA) and the direction of departure (DoD) [5,6,7,8,9]. To exploit the sparse scattering nature of mmWave channels, the sparse-based methods have been proposed to convert the channel estimation problems into the problems of sparse reconstruction. In the present papers, the sparsity of mmWave channel and the MC effect between antennas have not been considered simultaneously. By exploiting the channel sparsity in the spatial domain, a CS-based method is proposed to convert the problem of channel estimation into that of sparse reconstruction. CN ( a, B) denotes the complex Gaussian distribution with the mean being a and the variance matrix being B. k · k2 , ⊗, Tr {·}, vec {·}, (·)∗ , (·)T and (·)H denote the norm, the Kronecker product, the trace of a matrix, the vectorization of a matrix, the conjugate, the matrix transpose and the Hermitian transpose, respectively
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