Deep Learning-Based Channel Estimation Method for MIMO Systems in Spatially Correlated Channels

Lee, Sanggeun; Sim, Dongkyu

doi:10.1109/access.2024.3408894

IEEE Access

2024

DOI: 10.1109/access.2024.3408894

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Deep Learning-Based Channel Estimation Method for MIMO Systems in Spatially Correlated Channels

Sanggeun Lee,

Dongkyu Sim

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2024

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Cited by 2 publications

References 55 publications

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Adaptive Filters Versus Machine Learning Based Beam Tracking Techniques for Millimeter-Wave Wireless Communications Systems

Shallal Abbas Anooz,

Pourrostam,

Al-Ibadi

2024

IEEE Access

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Adaptive Filters Versus Machine Learning Based Beam Tracking Techniques for Millimeter-Wave Wireless Communications Systems

Shallal Abbas Anooz,

Pourrostam,

Al-Ibadi

2024

IEEE Access

View full text Add to dashboard Cite

CoCENet: Collaborative Channel Estimation Network for Rapidly Time-Varying UAV Systems

Zhou,

Zhang,

Yang

et al. 2024

Electronics

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Wireless communication capability is vital for remote transmission between unmanned aerial vehicle (UAV) coordination systems, but the communication link between highly dynamic UAV systems is unstable and severely disturbed due to its rapidly time-varying channel. Current channel estimation methods suffer from insufficient inter-symbol interference (ISI) and inter-carrier interference (ICI) suppression and inadequate noise filtering. Therefore, a collaborative channel estimation network (CoCENet) is proposed in this paper, and it can restrain the channel interference by capturing the amplitude–phase and time–frequency correlation at the same time. Moreover, CoCENet applies a multi-scale fusion strategy to optimize the purity of the estimated outcome. Our experiment results demonstrate that CoCENet has preferable performance in terms of the suppression of channel interference and noise in rapidly time-varying UAV systems in a complex environment without stationarity assumption. At a signal-to-noise ratio (SNR) of −10 dB, the mean square error (MSE) of CoCENet is improved by 1.7–2.3 dB compared to existing methods, and at a SNR of 20 dB, the MSE is improved by 1.1–2.2 dB.

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Deep Learning-Based Channel Estimation Method for MIMO Systems in Spatially Correlated Channels

Cited by 2 publications

References 55 publications

Adaptive Filters Versus Machine Learning Based Beam Tracking Techniques for Millimeter-Wave Wireless Communications Systems

Adaptive Filters Versus Machine Learning Based Beam Tracking Techniques for Millimeter-Wave Wireless Communications Systems

CoCENet: Collaborative Channel Estimation Network for Rapidly Time-Varying UAV Systems

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