參考文獻

[1] LECUN Y, BENGIO Y, HINTON G. Deep learning [J]. nature, 2015, 521(7553)： 436.

[2] WANG T Q, et al. Deep learning-based CSI feedback approach for time-varying massive MIMO channels [J]. IEEE Wireless Communications Letters, 2018,8(2)： 416-419.

[3] WEN C K, SHIH W T, JIN S. Deep learning for massive MIMO CSI feedback[J]. IEEE Wireless Communications Letters, 2018,7(5)：748-751.

[4] CAI Q Y, DONG C, NIUK Attention model for massive MIMO CSI compression feedback and recovery： 2019 IEEE Wireless Communications and Networking Conference (WCNC)[C]. New York：IEEE, 2019.

[5] LIAO Y, et al. ChanEstNet： A deep learning based channel estimation for high-speed scenarios： ICC 2019-2019 IEEE international conference on communications (ICC) [C]. New York：IEEE, 2019.

[6] SAMUEL N,DISKIN T, WIESEL A. Learning to detect [J]. IEEE Transactions on Signal Processing,2019,67(10)：2554-2564.

[7] GUO J J, et al. Convolutional neural network-based multiple-rate compressive sensing for massive MIMO CSI feedback： Design, simulation, and analysis [J]. IEEE Transactions on Wireless Communications ,2020,19(4)： 2827-2840.

[8] LIN B, et al. A novel OFDM autoencoder featuring CNN-based channel estimation for internet of vessels[J]. IEEE Internet of Things Journal, 2020, 7(8)： 7601-7611.

[9] MASHHADI M B,GüNDüZ D. Pruning the pilots： Deep learning-based pilot design and channel estimation for MIMO-OFDM systems [J]. IEEE Transactions on Wireless Communications ,2021,20(10)： 6315-6328.

[10] SARADHI P P, PANDYA R J, IYER S, et al. Deep Learning Oriented Channel Estima- tion for Interference Reduction for 5G：2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) [C]. New York： IEEE, 2021.