Deep Learning-based Channel Estimation for Beamspace mmWave Massive MIMO Systems

J. Brady, N. Behdad, and A. M. Sayeed, “Beamspace MIMO for millimeter-wave communications: System architecture, modeling, analysis, and measurements,” IEEE Trans. Antennas Propag. , vol. 61, no. 7, pp. 3814–3827, Jul. 2013

2013

Earlier work this paper cites.

A. L. Swindlehurst, E. Ayanoglu, P. Heydari, and F. Capolino, “Millimeter-wave massive MIMO: The next wireless revolution?” IEEE Comm. Mag. , vol. 52, no. 9, pp. 56–62, Sep. 2014

2014

Earlier work this paper cites.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition” in Proc IEEE Conf. Comput. Vis. Pattern Recognit. , Jun. 2016, pp. 770–778

2016

Earlier work this paper cites.

C. A. Metzler, A. Maleki, and R. G. Baraniuk, “From denoising to compressed sensing,” IEEE Trans. Inf. Theory , vol. 62, no. 9, pp. 5117–5144, Sept. 2016

2016

Earlier work this paper cites.

X. Gao, L. Dai, S. Han, C.-L. I, and X. Wang, “Reliable beamspace channel estimation for millimeter-wave massive MIMO systems with lens antenna array,” IEEE Trans. Wireless Commun. , vol. 16, no. 9, pp. 6010–6021, Sept. 2017

2017

Cited alongside, same era.

C. A. Metzler, Ali Mousavi, and R. G. Baraniuk, “Learned D-AMP: principled neural network based compressive image recovery,” arxiv preprint arXiv:1704.06625 , 2017

Original

2017

Cited alongside, same era.

K. Zhang, W. Zuo, Y. Chen, D. Meng, and L. Zhang, “Beyond a Gaussian denoiser: Residual learning of deep CNN for image denoising,” IEEE Trans. Image Process. , vol. 16, no. 9, pp. 3142–3155, Jul. 2017

2017

Cited alongside, same era.

T. Wang, C.-K. Wen, H. Wang, F. Gao, T. Jiang, and S. Jin, “Deep learning for wireless physical layer: Opportunities and challenges,” China Communications , vol. 14, no. 11, pp. 92–111, Nov. 2017

2017

Later among the works it cites.

J. Yang, C.-K. Wen, S. Jin, F. Gao, “Beamspace channel estimation in mmWave systems via cosparse image reconstruction technique,” to appear in IEEE Trans. Commun. , 2018

2018

Closest in time.

H. Ye, G. Y. Li, and B.-H. F. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wireless Commun. Lett. ,vol. 7, no. 1, pp. 114–117, Feb. 2018

2018

Closest in time.