T. Schenk, RF imperfections in high-rate wireless systems: impact and digital compensation (Springer Science and Business Media, Berlin, 2008). https://doi.org/10.1007/978-1-4020-6903-1
T. O’Shea, J. Hoydis, An introduction to deep learning for the physical layer. IEEE Trans. Cogn. Commun. Netw. 3(4), 563–575 (2017). https://doi.org/10.1109/TCCN.2017.2758370
K. He, X. Zhang, S. Ren, J. Sun, Delving deep into rectifiers: surpassing human-level performance on imagenet classification. 2015 IEEE International Conference on Computer Vision (ICCV), 1026–1034 (2015) https://doi.org/10.1109/ICCV.2015.123
K. Hornik, M. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989). https://doi.org/10.1016/0893-6080(89)90020-8
T. Wang, C.-K. Wen, H. Wang, F. Gao, T. Jiang, S. Jin, Deep learning for wireless physical layer: opportunities and challenges. China Commun. 14(11), 92–111 (2017). https://doi.org/10.1109/CC.2017.8233654
E. Nachmani, E. Marciano, D. Burshtein, Y. Be’ery, RNN decoding of linear block codes. arXiv:abs/1702.07560 (2017)
Z. Qin, H. Ye, G.Y. Li, B.-H.F. Juang, Deep learning in physical layer communications. IEEE Wirel. Commun. 26(2), 93–99 (2019). https://doi.org/10.1109/MWC.2019.1800601
V. Raj, S. Kalyani, Backpropagating through the air: deep learning at physical layer without channel models. IEEE Commun. Lett. 22(11), 2278–2281 (2018). https://doi.org/10.1109/LCOMM.2018.2868103
S. Dörner, S. Cammerer, J. Hoydis, St. Brink, Deep learning based communication over the air. IEEE J. Sel. Top. Signal Process. 12(1), 132–143 (2018). https://doi.org/10.1109/JSTSP.2017.2784180
M. Ibukahla, J. Sombria, F. Castanie, N.J. Bershad, Neural networks for modeling nonlinear memoryless communication channels. IEEE Trans. Commun. 45(7), 768–771 (1997). https://doi.org/10.1109/26.602580
J. Sjoberg, Q. Zhang, L. Ljung, A. Benveniste, B. Delyon, P.-Y. Glorennec, H. Hjalmarsson, A. Juditsky, Nonlinear black-box modeling in system identification: a unified overview. Automatica 31(12), 1691–1724 (1995). https://doi.org/10.1016/0005-1098(95)00120-8. (Trends in System Identification)
Article MathSciNet Google Scholar
S. Cammerer, T. Gruber, J. Hoydis, S. Brink, Scaling deep learning-based decoding of polar codes via partitioning. GLOBECOM 2017 - 2017 IEEE Global Communications Conference, 1–6 (2017)
X. Gao, S. Jin, C.-K. Wen, G.Y. Li, Comnet: combination of deep learning and expert knowledge in OFDM receivers. IEEE Commun. Lett. 22, 2627–2630 (2018)
H. He, C.-K. Wen, S. Jin, G.Y. Li, Deep learning-based channel estimation for beamspace mmwave massive MIMO systems. IEEE Wirel. Commun. Lett. 7(5), 852–855 (2018). https://doi.org/10.1109/LWC.2018.2832128
M. Soltani, V. Pourahmadi, A. Mirzaei, H. Sheikhzadeh, Deep learning-based channel estimation. IEEE Commun. Lett. 23(4), 652–655 (2019). https://doi.org/10.1109/LCOMM.2019.2898944
H. Ye, G.Y. Li, B.-H. Juang, Power of deep learning for channel estimation and signal detection in OFDM systems. IEEE Wirel. Commun. Lett. 7(1), 114–117 (2018). https://doi.org/10.1109/LWC.2017.2757490
Q. Hu, F. Gao, H. Zhang, S. Jin, G.Y. Li, Deep learning for channel estimation: interpretation, performance, and comparison. IEEE Trans. Wirel. Commun. 20(4), 2398–2412 (2021). https://doi.org/10.1109/TWC.2020.3042074
H. Huang, J. Yang, H. Huang, Y. Song, G. Gui, Deep learning for super-resolution channel estimation and DOA estimation based massive MIMO system. IEEE Trans. Veh. Technol. 67(9), 8549–8560 (2018). https://doi.org/10.1109/TVT.2018.2851783
Q. Bai, J. Wang, Y. Zhang, J. Song, Deep learning-based channel estimation algorithm over time selective fading channels. IEEE Trans. Cogn. Commun. Netw. 6(1), 125–134 (2020). https://doi.org/10.1109/TCCN.2019.2943455
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, Generative adversarial nets, in Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
X. Liao, J. Si, J. Shi, Z. Li, H. Ding, Generative adversarial network assisted power allocation for cooperative cognitive covert communication system. IEEE Commun. Lett. 24(7), 1463–1467 (2020). https://doi.org/10.1109/LCOMM.2020.2988384
A.T.Z. Kasgari, W. Saad, M. Mozaffari, H.V. Poor, Experienced deep reinforcement learning with generative adversarial networks (GANs) for model-free ultra reliable low latency communication. IEEE Trans. Commun. 69(2), 884–899 (2021). https://doi.org/10.1109/TCOMM.2020.3031930
Y. Yang, Y. Li, W. Zhang, F. Qin, P. Zhu, C.-X. Wang, Generative-adversarial-network-based wireless channel modeling: challenges and opportunities. IEEE Commun. Mag. 57(3), 22–27 (2019). https://doi.org/10.1109/MCOM.2019.1800635
L. Sun, Y. Wang, A.L. Swindlehurst, X. Tang, Generative-adversarial-network enabled signal detection for communication systems with unknown channel models. IEEE J. Sel. Areas Commun. 39(1), 47–60 (2021). https://doi.org/10.1109/JSAC.2020.3036954
H. Ye, L. Liang, G.Y. Li, B.-H. Juang, Deep learning-based end-to-end wireless communication systems with conditional GANs as unknown channels. IEEE Trans. Wirel. Commun. 19(5), 3133–3143 (2020). https://doi.org/10.1109/TWC.2020.2970707
H. Ye, G.Y. Li, B.-H.F. Juang, K. Sivanesan, Channel agnostic end-to-end learning based communication systems with conditional GAN. 2018 IEEE Globecom Workshops (GC Wkshps), 1–5 (2018) https://doi.org/10.1109/GLOCOMW.2018.8644250
Q. Zhang, A. Ferdowsi, W. Saad, M. Bennis, Distributed conditional generative adversarial networks (GANs) for data-driven millimeter wave communications in UAV networks. IEEE Trans. Wirel. Commun. 21(3), 1438–1452 (2022). https://doi.org/10.1109/TWC.2021.3103971
Z. Ghassemlooy, W. Popoola, W. Rajbhandari, Optical Wireless Communications: System and Channel Modeling with MATLAB (CRC Press, Boca Raton, 2012)
S. Arnon, J.R. Barry, G.K. Karagiannidis, R. Schober, M. Uysal, Advanced Optical Wireless Communication (Cambridge University Press, Cambridge, 2012)
A.A. Farid, S. Hranilovic, Outage capacity optimization for free-space optical links with pointing errors. J. Lightwave Technol. 25, 1702–1710 (2007). https://doi.org/10.1109/JLT.2007.899174
E. Lee, J. Park, D. Han, G. Yoon, Performance analysis of the asymmetric dual-hop relay transmission with mixed RF/FSO links. IEEE Photonics Technol. Lett. 23(21), 1642–1644 (2011). https://doi.org/10.1109/LPT.2011.2166063
A. Upadhya, V.K. Dwivedi, G. Singh, Relay-aided free-space optical communications using αμ distribution over atmospheric turbulence channels with misalignment errors. Opt. Commun. 416, 117–124 (2018). https://doi.org/10.1016/j.optcom.2018.01.053
A. Upadhya, V.K. Dwivedi, G. Singh, Multiuser diversity for mixed RF/FSO cooperative relaying in the presence of interference. Opt. Commun. 442, 77–83 (2019)
A. Upadhya, V.K. Dwivedi, M.-S. Alouini, Interference-limited mixed MUD-RF/FSO two-way cooperative networks over double generalized gamma turbulence channels. IEEE Commun. Lett. 442, 1551–1555 (2019)
A. Goel, A. Upadhya, V.K. Dwivedi, Diversity aided millimeter-wave/free space optical cooperative relaying systems. Int. J. Commun. Syst. 34(4), 4700 (2021). https://doi.org/10.1002/dac.4700
A. Upadhya, Investigation of mixed RF/FSO decode-and-forward NOMA cooperative relaying networks. Wirel. Pers. Commun. 124, 2923–2938 (2022). https://doi.org/10.1007/s11277-022-09496-2
A. Goel, R. Bhatia, Hybrid RF/MIMO-FSO relaying systems over Gamma–Gamma fading channels. International Conference on Innovative Computing and Communications, in Advances in Intelligent Systems and Computing, vol 1165. Springer, Singapore 1165, (2021)
A. Goel, R. Bhatia, On the performance of mixed user diversity-RF/spatial diversity-FSO cooperative relaying AF systems. Opt. Commun. 477, 126333 (2020)
A. Goel, R. Bhatia, Double relay selection for combating the impact of interference and hardware impairments in mixed RF/FSO two way relay networks. Opt. Quant. Electron. 54(11), 751 (2022)
W.R. Braun, U. Dersch, A physical mobile radio channel model. IEEE Trans. Veh. Technol. 40(2), 472–482 (1991)
S. Song, Y. Liu, T. Xu, S. Liao, L. Guo, Demonstration of channel-predictable free space optical communication system using machine learning, in 2021 Optical Fiber Communications Conference and Exhibition (OFC), 1–3 (2021)
P. Mishra, A. Sonali, Dixit, V.K. Jain, Machine learning techniques for channel estimation in free space optical communication systems, in 2019 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 1–6 (2019) https://doi.org/10.1109/ANTS47819.2019.9117976
L. Li, T. Bu, Y. Li, S. Wei, A. Harris, Z. Chen, K. Foo, D. Shen, G. Chen, Machine learning based tool chain solution for free space optical communication (FSOC) propagation modeling. https://doi.org/10.1109/AERO50100.2021.9438522
A. Upadhya, Gan based channel estimation and interference cancellation for mixed RF/FSO cooperative relaying systems. Phys. Commun. 61, 102199 (2023). https://doi.org/10.1016/j.phycom.2023.102199
M.A. Kashani, M. Uysal, M. Kavehrad, A novel statistical channel model for turbulence-induced fading in free-space optical systems. IEEE/OSA J. Lightwave Technol. 33(11), 2303–2312 (2015)
留言 (0)