Deep learning based frequency-selective channel estimation for hybrid mmWave MIMO Systems
Deep learning based frequency-selective channel estimation for hybrid mmWave MIMO Systems
Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. {However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation.} The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of $\unit[\{4-10\}]{dB}$ with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only $\unit[\{1-1.5\}]{dB}$, while significantly reducing complexity by two orders of magnitude.
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
22 February 2021
Celik, Abdulkadir
f8e72266-763c-4849-b38e-2ea2f50a69d0
Celik, Abdulkadir
(2021)
Deep learning based frequency-selective channel estimation for hybrid mmWave MIMO Systems.
CoRR.
Abstract
Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems typically employ hybrid mixed signal processing to avoid expensive hardware and high training overheads. {However, the lack of fully digital beamforming at mmWave bands imposes additional challenges in channel estimation. Prior art on hybrid architectures has mainly focused on greedy optimization algorithms to estimate frequency-flat narrowband mmWave channels, despite the fact that in practice, the large bandwidth associated with mmWave channels results in frequency-selective channels. In this paper, we consider a frequency-selective wideband mmWave system and propose two deep learning (DL) compressive sensing (CS) based algorithms for channel estimation.} The proposed algorithms learn critical apriori information from training data to provide highly accurate channel estimates with low training overhead. In the first approach, a DL-CS based algorithm simultaneously estimates the channel supports in the frequency domain, which are then used for channel reconstruction. The second approach exploits the estimated supports to apply a low-complexity multi-resolution fine-tuning method to further enhance the estimation performance. Simulation results demonstrate that the proposed DL-based schemes significantly outperform conventional orthogonal matching pursuit (OMP) techniques in terms of the normalized mean-squared error (NMSE), computational complexity, and spectral efficiency, particularly in the low signal-to-noise ratio regime. When compared to OMP approaches that achieve an NMSE gap of $\unit[\{4-10\}]{dB}$ with respect to the Cramer Rao Lower Bound (CRLB), the proposed algorithms reduce the CRLB gap to only $\unit[\{1-1.5\}]{dB}$, while significantly reducing complexity by two orders of magnitude.
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Published date: 22 February 2021
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Local EPrints ID: 505477
URI: http://eprints.soton.ac.uk/id/eprint/505477
PURE UUID: 80541802-9c95-48bf-ac7e-d3d93b1a7918
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Date deposited: 09 Oct 2025 17:02
Last modified: 10 Oct 2025 02:15
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Author:
Abdulkadir Celik
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