A Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the "beam squint" effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems....

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 23; no. 5; p. 2638
Main Authors Liu, Qingli, Li, Yangyang, Sun, Jiaxu
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 27.02.2023
MDPI
Subjects
Accuracy
Algorithms
Antennas
beam squint
Broadband
Broadband transmission
channel estimation
Communications networks
Communications systems
Convergence
Domains
Energy efficiency
massive MIMO
millimeter wave
Millimeter wave communication systems
Millimeter waves
MIMO communication
MIMO communications
model driven
Noise reduction
Shrinkage
Signal to noise ratio
Sparse matrices
Wireless communications
China
massive MIMO
beam squint
millimeter wave
channel estimation
model driven
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Summary:Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the "beam squint" effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the "beam squint" effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23052638