[1] |
ZENG Y , NG T . A semi-blind channel estimation method for multiuser multiantenna OFDM systems[J]. IEEE Transactions on Signal Processing, 2004,52(5): 1419-1429.
|
[2] |
DORNER S , CAMMERER S , HOYDIS J ,et al. Deep learning based communication over the air[J]. IEEE Journal of Selected Topics in Signal Processing, 2018,12(1): 132-143.
|
[3] |
HE Y , ZHANG J , WEN C ,et al. TurboNet:amodel-driven DNN decoder based on max-log-MAP algorithm for turbo code[C]// IEEE VTS Asia Pacific Wireless Communications Symposium,Piscataway:IEEE Press, 2019: 1-5.
|
[4] |
WEN C , SHIH W , JIN S . Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018,7(5): 748-751.
|
[5] |
GAO X , JIN S , WEN C ,et al. ComNet:combination of deep learning and expert knowledge in OFDM receivers[J]. IEEE Communications Letters, 2018,22(12): 2627-2630.
|
[6] |
SCHMIDHUBER J . Deep learning in neural networks:an overview[J]. Neural Networks, 2015(61): 85-117.
|
[7] |
DING C , LOY C C , HE K ,et al. Learning a deep convolutional network for image super-resolution[C]// Proceedings of ECCV. Cham:Springer, 2014: 184-199.
|
[8] |
KIM J , LEE J K , LEE K M . Accurate image super-resolution using very deep convolutional networks[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,Piscataway:IEEE Press, 2016: 1646-1654.
|
[9] |
KIM J , LEE J K , LEE K M . Deeply-recursive convolutional network for image super-resolution[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 1637-1645.
|
[10] |
LIM B,SON S , KIM H , NAH S ,et al. Enhanced deep residual networks for single image super-resolution[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops. Piscataway:IEEE Press, 2017: 1132-1140.
|
[11] |
LEDIG C , THEIS L , HUSZAR F ,et al. Photo-realistic single image super-resolution using a generative adversarial network[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 105-114.
|
[12] |
DONOHO D L . Compressed sensing[J]. IEEE Transactions on Information Theory, 2006,52(4): 1289-1306.
|
[13] |
FOUCART S , RAUHUT H . A mathematical introduction to compressive sensing[M]. New York: Springer-VerlagPress, 2013.
|
[14] |
MOUSAVI A , PATEL A B , BARANIUK R G . A deep learning approach to structured signal recovery[C]// Proceedings of 53rd Annual Allerton Conference on Communication,Control,and Computing. Piscataway:IEEE Press, 2015: 1336-1343.
|
[15] |
YAO H T , DAI F , ZHANG D M ,et al. DR2-Net:deep residual reconstruction network for image compressive sensing[J]. Neurocomputing, 2019,359: 483-493.
|
[16] |
KULKARNI K , LOHIT S , TURAGA P ,et al. ReconNet:non-iterative reconstruction of images from compressively sensed measurements[C]// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 449-458.
|
[17] |
DONG P , ZHANG H , LI G Y ,et al. Deep CNN for wideband mmWave massive mimo channel estimation using frequency correlation[C]// IEEE International Conference on Acoustics,Speech and Signal Processing. Piscataway:IEEE Press, 2019: 4529-4533.
|
[18] |
JIN Y , ZHANG J , AI B ,et al. Channel estimation for mmWave massive MIMO with convolutional blind denoising network[J]. IEEE Communications Letters, 2020,24(1): 95-98.
|
[19] |
BAI Q , WANG J , ZHANG Y ,et al. Deep learning-based channel estimation algorithm over time selective fading channels[J]. IEEE Transactions on Cognitive Communications and Networking, 2020,6(1): 125-134.
|
[20] |
LIAO Y , HUA Y , DAI X ,et al. ChanEstNet:adeep learning based channel estimation for high-speed scenarios[C]// Proceedings of IEEE International Conference on Communications. Piscataway:IEEE Press, 2019: 1-6.
|
[21] |
SHI Q , LIU Y , ZHANGS S ,et al. Channel estimation for Wi-Fi prototype systems with super-resolution image recovery[C]// Proceedings of IEEE International Conference on Communications. Piscataway:IEEE Press, 2019: 1-6.
|
[22] |
SOLTANI M , POURAHMADI V , MIRZAEI A ,et al. Deep learning-based channel estimation[J]. IEEE Communications Letters, 2019,23(4): 652-655.
|
[23] |
LI L , CHEN H , CHANG H ,et al. Deep residual learning meets OFDM channel estimation[J]. IEEE Wireless Communications Letters, 2020,9(5): 615-618.
|
[24] |
HE H , WEN C K , JIN S ,et al. Deep learning-based channel estimation for beamspace mmWave massive MIMO systems[J]. IEEE Wireless Communications Letters, 2018,7(5): 852-855.
|
[25] |
WEI Y , ZHAO M , ZHAO M ,et al. An AMP-based network with deep residual learning for mmWave beamspace channel estimation[J]. IEEE Wireless Communications Letters, 2019,8(4): 1289-1292.
|
[26] |
MA W , QI C , ZHANG Z ,et al. Deep learning for compressed sensing based channel estimation in millimeter wave massive MIMO[C]// Proceedings of International Conference on Wireless Communications and Signal Processing. Piscataway:IEEE Press, 2019: 1-6.
|