參考文獻 |
[1] A. J. Goldsmith and S. . Chua, “Adaptive coded modulation for fading channels,” IEEE Transactions on Communications, vol. 46, no. 5, pp. 595–602, 1998.
[2] B. Vucetic, “An adaptive coding scheme for time-varying channels,” IEEE Transactions on Communications, vol. 39, no. 5, pp. 653–663, 1991.
[3] J. G. Lee, S. Sen, and R. ETKIN, “Transmit antenna selection,” patenteu. [Online]. Available: https://patents.google.com/patent/US9768924/en
[4] NR; Study on New Radio (NR) to support non-terrestrial networks, 3GPP, Jun. 2018, TS 38.811 V15.1.0.
[5] J. B. Andersen, J. Jensen, S. H. Jensen, and F. Frederiksen, “Prediction of future fading based on past measurements,” in Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324), vol. 1, 1999.
[6] R. O. Adeogun, P. D. Teal, and P. A. Dmochowski, “Extrapolation of mimo mobile-to-mobile wireless channels using parametric-model-based prediction,” IEEE Transactions on Vehicular Technology, vol. 64, no. 10, pp. 4487–4498, 2015.
[7] L. Liu, H. Feng, T. Yang, and B. Hu, “Mimo-ofdm wireless channel prediction by exploiting spatial-temporal correlation,” IEEE Transactions on Wireless Communications, vol. 13, no. 1, pp. 310–319, 2014.
[8] H. Hallen, A. Duel-Hallen, Shengquan Hu, Tung-Shen Yang, and Ming Lei, “A physical model for wireless channels to provide insights for long range prediction,” in MILCOM 2002. Proceedings, vol. 1, 2002.
[9] A. Duel-Hallen, “Fading channel prediction for mobile radio adaptive transmission systems,” Proceedings of the IEEE, vol. 95, no. 12, pp. 2299–2313, 2007.
[10] A. Heidari, A. K. Khandani, and D. Mcavoy, “Adaptive modelling and longrange prediction of mobile fading channels,” IET Communications, vol. 4, no. 1, pp. 39–50, 2010.
[11] T. Mikolov, M. Karafiát, L. Burget, J. Cernocký, and S. Khudanpur, “Recurrent neural network based language model,” vol. 2, Jan. 2010.
[12] W. Jiang and H. D. Schotten, “A comparison of wireless channel predictors: Artificial intelligence versus kalman filter,” in ICC 2019 2019-IEEE International Conference on Communications (ICC), 2019.
[13] C. Luo, J. Ji, Q. Wang, X. Chen, and P. Li, “Channel state information prediction for 5g wireless communications: A deep learning approach,” IEEE Transactions on Network Science and Engineering, vol. 7, no. 1, pp. 227–236, 2020.
[14] Y. Zhu, X. Dong, and T. Lu, “An adaptive and parameter-free recurrent neural structure for wireless channel prediction,” IEEE Transactions on Communications, vol. 67, no. 11, pp. 8086–8096, 2019.
[15] System Tool Kit 11, Analytical Graphics, Inc., 2019, [Online; accessed 27-July-2020]. [Online]. Available: https://www.agi.com/products/stk
[16] K. E. Baddour and N. C. Beaulieu, “Autoregressive modeling for fading channel simulation,” IEEE Transactions on Wireless Communications, vol. 4, no. 4, pp. 1650–1662, 2005.
[17] T. Ding and A. Hirose, “Fading channel prediction based on combination of complex-valued neural networks and chirp z-transform,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 9, pp. 1686–1695, 2014.
[18] M. F. Pop and N. C. Beaulieu, “Limitations of sum-of-sinusoids fading channel simulators,” IEEE Transactions on Communications, vol. 49, no. 4, pp. 699–708, 2001.
[19] Y. Zhao, H. Gao, N. C. Beaulieu, Z. Chen, and H. Ji, “Echo state network for fast channel prediction in ricean fading scenarios,” IEEE Communications Letters, vol. 21, pp. 672–675, 2017.
[20] L. Bai, C. Wang, G. Goussetis, S. Wu, Q. Zhu, W. Zhou, and E. M. Aggoune, “Channel modeling for satellite communication channels at q-band in high latitude,” IEEE Access, vol. 7, pp. 137 691–137 703, 2019.
[21] R. Dey and F. M. Salem, “Gate-variants of gated recurrent unit (gru) neural networks,” in 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS), 2017.
[22] J. Kang, W. Zhang, and J. Liu, “Gated recurrent units based hybrid acoustic models for robust speech recognition,” in 2016 10th International Symposium on Chinese Spoken Language Processing (ISCSLP), 2016.
[23] NR; Study on New Radio Access Technology Physical Layer Aspects, 3GPP, Sep. 2017, TS 38.802 V14.2.0.
[24] S. Coleri, M. Ergen, A. Puri, and A. Bahai, “Channel estimation techniques based on pilot arrangement in ofdm systems,” IEEE Transactions on Broadcasting, vol. 48, no. 3, pp. 223–229, 2002.
[25] L. Liu, C. Oestges, J. Poutanen, K. Haneda, P. Vainikainen, F. Quitin, F. Tufvesson, and P. D. Doncker, “The cost 2100 mimo channel model,” IEEE Wireless Communications, vol. 19, no. 6, pp. 92–99, 2012.
[26] P. Kyösti, J. Meinilä, L. Hentila, X. Zhao, T. Jämsä, C. Schneider, M. Narandzic, M. Milojević, A. Hong, J. Ylitalo, V.M. Holappa, M. Alatossava, R. Bultitude, Y. Jong, and T. Rautiainen, “Winner ii channel models,” IST-4-027756 WINNER II D1.1.2 V1.2, Feb. 2008.
[27] Spatial channel model for MIMO simulations, 3GPP, Jun. 2018, TR 25.996 V15.0.0.
[28] Pei-Yun Tsai and Tzi-Dar Chiueh, “Frequency-domain interpolation-based channel estimation in pilot-aided ofdm systems,” in 2004 IEEE 59th Vehicular Technology Conference. VTC 2004Spring (IEEE Cat. No.04CH37514), vol. 1, 2004.
[29] Wikipedia contributors, “Rayleigh fading — Wikipedia, the free encyclopedia,” https://en.wikipedia.org/w/index.php?title=Rayleigh_fading&oldid=935568613, 2020, [Online; accessed 18-August-2020].
[30] Attenuation due to clouds and fog, Int. Telecommun., Aug. 2018, iTU-R 840-7.
[31] Propagation data and prediction methods required for the design of earth-space telecommunication systems,, Int. Telecommun., Jul. 2015, iTU-R 618-12.
[32] D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations, Dec. 2014.
[33] A. Putra, B. A. Sumbodo, and A. Nurbaqin, “Satellite tracking control system for ugm ground station based on tle calculation,” Jan. 2016, pp. 91–96.
[34] NR; Requirements for support of radio resource management, 3GPP, Jul. 2018, TS 38.133 V15.2.0. |