Abstract
The compressive sensing (CS)-based sparse channel estimator is recognized as the most effective solution to the excessive pilot overhead in massive MIMO systems. However, due to the complex signal processing in the wireless communication systems, the measurement matrix in the CS-based channel estimation is sometimes “unfriendly” to the channel recovery. To overcome this problem, in this paper, the state-of-the-art sparse Bayesian learning using approximate message passing with unitary transformation (UTAMP-SBL), which is robust to various measurement matrices, is leveraged to address the multi-user uplink channel estimation for hybrid architecture millimeter wave massive MIMO systems. Specifically, the sparsity of channels in the angular domain is exploited to reduce the pilot overhead. Simulation results demonstrate that the UTAMP-SBL is able to achieve effective performance improvement than other competitors with low pilot overhead.
Highlights
Hybrid Millimeter Wave MassiveMassive MIMO is a key technology for the fifth-generation (5G) communication systems [1]
Thanks to the shorter wavelength of the millimeter wave signal, the numerous antennas are packed into a compact-size array, which facilitates the commercial deployment of massive MIMO systems [2], but under the consideration of the high power consumption of Analog-to-Digital Converters (ADCs), the hybrid beamforming architecture, that divides the precoder and the combiner into the analog and digital domains, is developed to solve it and regarded as an effective alternative [3,4]
In the channel estimation, the measurement matrix Φ p derives from pilots, precoders, combiners and the partial discrete Fourier transform (DFT) matrices, so it is a difficult matrix in all probability
Summary
Massive MIMO is a key technology for the fifth-generation (5G) communication systems [1]. Thanks to the shorter wavelength of the millimeter wave (mmWave) signal, the numerous antennas are packed into a compact-size array, which facilitates the commercial deployment of massive MIMO systems [2], but under the consideration of the high power consumption of Analog-to-Digital Converters (ADCs), the hybrid beamforming architecture, that divides the precoder and the combiner into the analog and digital domains, is developed to solve it and regarded as an effective alternative [3,4]. In [12], the authors utilize the low-rank structure along with the sparsity in angular domain to improve the channel estimation performance, and the off-grid angles can be recovered with their algorithm successfully. A novel super-resolution downlink channel estimation approach developed from the SBL is provided in [15], where the sampled angular grid points are treated as the underlying parameters The performance of these CS-based algorithms is affected by the measurement matrix which is usually associated with the signal processing operations in the system. ∝ denotes equality up to a constant scale factor
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