Modified Gaussian Mixture Distribution-Based Deep Learning Technique for Beamspace Channel Estimation in mmWave Massive MIMO Systems
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Publisher
Springer Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-16-9885-9_32
Reference21 articles.
1. Wei Y, Zhao MM, Zhao M, Lei M, Yu Q (2019) An AMP-based network with deep residual learning for mmWave beamspace channel estimation. IEEE Wirel Commun Lett 8(4):1289–1292
2. Vlachos E, Alexandropoulos GC, Thompson J (2018) Massive MIMO channel estimation for millimeter wave systems via matrix completion. IEEE Signal Process Lett 25(11):1675–1679
3. Donoho DL, Maleki A, Montanari A (2010) Message passing algorithms for compressed sensing: I. motivation and construction. In Proceedings of information theory workshop, (ITW’10), Cairo, Egypt, pp 1–5
4. Dai R, Liu Y, Wang Q, et al (2021) Channel estimation by reduced dimension decomposition formillimeter wave massive MIMO system. Phys Commun 44
5. Chun C-J, Kang J-M, Kim I-M (2018) Deep learning based channel estimation for massive MIMO systems. IEEE Wireless Commun 6(8):245–267
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Deep Learning-based Efficient Beamspace Estimation Approach in Millimeter-Wave Massive MIMO Systems;2023 6th International Conference on Electrical Information and Communication Technology (EICT);2023-12-07
2. Sparsifying Dictionary Learning for Beamspace Channel Representation and Estimation in Millimeter-Wave Massive MIMO;IEEE Access;2023
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