Zacytuj

T. Hwang, C. Yang, G. Wu, S. Li and G. Li, “OFDM and its wireless applications: A survey”, IEEE Trans. Vehicular Technol., vol. 58, no. 4, pp. 1673-1694, 2009 . Search in Google Scholar

Tao Cui and C. Tellambura, “Joint data detection and channel estimation for OFDM systems”, IEEE Transactions on Communications, vol. 54, no. 4, pp. 670-679, 2006 . Search in Google Scholar

Y. Li, J. Winters and N. Sollenberger, “MIMO-OFDM for wireless communication: Signal detection with enhanced channel estimation”, IEEE Trans. Commun., pp. 1471-1477, 2002 . Search in Google Scholar

J. K. Moon and S. I. Choi, “Performance of channel estimation methods for OFDM systems in a multipath fading channels”, IEEE Trans. Consum. Electron, vol. 46, no. 1, pp. 161-170, 2000 . Search in Google Scholar

M. K. Ozdemir and H. Arslan, “Channel estimation for wireless OFDM systems”, Commun. Surveys Tuts., vol. 9, no. 2, pp. 18-48, Second Quarter 2007 . Search in Google Scholar

S. Coleri, M. Ergen, A. Puri and A. Bahai, “Channel estimation techniques based on pilot arrangement in OFDM systems”, IEEE Trans. Broadcast., vol. 48, no. 3, pp. 223-229, 2002 . Search in Google Scholar

Tian-Ming Ma, Yu-Song Shi, and Ying-Guan Wang, “A Low Complexity MMSE for OFDM Systems over Frequency-Selective Fading Channels”, IEEE Communications Letters, vol. 16, no. 3, 2012 . Search in Google Scholar

S. M. Aldossari and K.-C. Chen, “Machine learning for wireless communication channel modeling: an overview”, Wireless Personal Communications, vol. 106, no. 1, pp. 46–70, 2019 . Search in Google Scholar

W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, and F. E. Alsaadi, “A survey of deep neural network architectures and their applications”, Neurocomputing, vol. 234, pp. 11–26, 2017 . Search in Google Scholar

Zhang, Chaoyun, Paul Patras, and Hamed Haddadi, “Deep learning in mobile and wireless networking: A survey”, IEEE Communications surveys & tutorials, vol.21, no.3, pp. 2224-2287, 2019 . Search in Google Scholar

L. Dai et al., “Deep learning for wireless communications: An emerging interdisciplinary paradigm”, IEEE Wireless Commun., vol. 27, no. 4, pp. 133–139, 2020 . Search in Google Scholar

H. Huang et al., “Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions”, IEEE Wireless Commun., vol. 27, no. 1, pp.214–222, 2020 . Search in Google Scholar

Le. H. A., Van. Chien. T., Nguyen. T. H., Choo. H. and Nguyen. V. D, “Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems”, Sensors, vol. 21, no. 14, 2021. Search in Google Scholar

A. K. Gizzini, M. Chafii, A. Nimr and G. Fettweis, “Deep Learning Based Channel Estimation Schemes for IEEE 802.11p Standard”, IEEE Access, vol. 8, pp.113751–113765, 2020. Search in Google Scholar

R. Jiang, X. Wang, S. Cao, J. Zhao and X. Li, “Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems”, IEEE Access, vol. 7, pp.23579–23594, 2019 . Search in Google Scholar

M. H. Essai Ali, “Deep learning-based pilot-assisted channel state estimator for OFDM systems”, IET Communications, vol. 15, no. 2, pp. 257-264, 2021 . Search in Google Scholar

L. Li, H. Chen, H.-H. Chang, and L. Liu, “Deep residual learning meets OFDM channel estimation”, IEEE Wireless Commun. Lett., vol. 9, no. 5, pp. 615-618, 2020. Search in Google Scholar

Z. Zhao, M. C. Vuran, F. Guo and S. D. Scott, “Deep-Waveform: A Learned OFDM Receiver Based on Deep Complex-Valued Convolutional Networks”, IEEE Journal on Selected Areas in Communications, vol. 39, no. 8, pp. 2407-2420, 2021. Search in Google Scholar

Liao, Yong, et al., “ChanEstNet: A deep learning based channel estimation for high-speed scenarios”, in Proc. IEEE Int. Commun. Conf. (ICC), Shanghai, China, May 2019, pp. 1–6 . Search in Google Scholar

G. Pan, Z. Liu, W. Wang and M. Li, “A Signal Detection Scheme Based on Deep Learning in OFDM Systems”, IEEE 32nd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1–6, 2021 . Search in Google Scholar

S. Wang, R. Yao, T. A. Tsiftsis, N. I. Miridakis and N. Qi, “Signal Detection in Uplink Time-Varying OFDM Systems Using RNN With Bidirectional LSTM”, IEEE Wireless Communications Letters, vol. 9, no. 11, pp. 1947-1951, Nov. 2020 . Search in Google Scholar

A. K. Nair and V. Menon, “Joint Channel Estimation and Symbol Detection in MIMO-OFDM Systems: A Deep Learning Approach using Bi-LSTM,” 14th International Conference on Communication Systems & NETworkS (COMSNETS), pp. 406-411, 2022. Search in Google Scholar

Huang, S. C., and Le, T. H., Principles and Labs for Deep Learning, Academic Press: Cambridge, MA, USA, 2021 . Search in Google Scholar

H. Ye, G. Y. Li, and B. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems”, IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114-117, 2018 . Search in Google Scholar

Dey, Rahul, and Fathi M. Salem, “Gate-variants of gated recurrent unit (GRU) neural networks”, IEEE 60th international midwest symposium on circuits and systems (MWSCAS), pp. 1597-1600, 2017 . Search in Google Scholar

S. Hochreiter and J. Schmidhuber, “Long short-term memory”, Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997 . Search in Google Scholar

M. Ravanelli et al., “Light gated recurrent units for speech recognition”, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 2, pp. 92-102, 2018 . Search in Google Scholar

Jais, Imran Khan Mohd, Amelia Ritahani Ismail, and Syed Qamrun Nisa, “Adam optimization algorithm for wide and deep neural network”, Knowledge Engineering and Data Science, vol. 2, no. 1, pp. 41-46, 2019 . Search in Google Scholar

M. H. Essai and I. B. Taha, “Channel state information estimation for 5G wireless communication systems: recurrent neural networks approach”, PeerJ Computer Science 7, 2021, e682 . Search in Google Scholar

eISSN:
1339-309X
Język:
Angielski
Częstotliwość wydawania:
6 razy w roku
Dziedziny czasopisma:
Engineering, Introductions and Overviews, other