Auswahl der wissenschaftlichen Literatur zum Thema „Nonlinearity equalization“
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Zeitschriftenartikel zum Thema "Nonlinearity equalization"
Golani, Ori, Meir Feder und Mark Shtaif. „Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications“. Applied Sciences 9, Nr. 3 (02.02.2019): 511. http://dx.doi.org/10.3390/app9030511.
Der volle Inhalt der QuelleChen, Yuanjie. „Blind equalization with criterion with memory nonlinearity“. Optical Engineering 31, Nr. 6 (1992): 1200. http://dx.doi.org/10.1117/12.57519.
Der volle Inhalt der QuelleChen, Qianwen, Xiong Chen, David J. Pommerenke und Ming Yu. „Balanced Intermodulation Reference With Flat Frequency Response Using Nonlinearity Equalization“. IEEE Transactions on Electromagnetic Compatibility 62, Nr. 6 (Dezember 2020): 2634–37. http://dx.doi.org/10.1109/temc.2020.2981462.
Der volle Inhalt der QuelleMiao, Pu, Weibang Yin, Hui Peng und Yu Yao. „Study of the Performance of Deep Learning-Based Channel Equalization for Indoor Visible Light Communication Systems“. Photonics 8, Nr. 10 (18.10.2021): 453. http://dx.doi.org/10.3390/photonics8100453.
Der volle Inhalt der QuelleRuqi Zhang, Ruqi Zhang, Jianfeng Li Jianfeng Li, Zhitong Huang Zhitong Huang und Yuefeng Ji Yuefeng Ji. „Adaptive frequency domain pre-equalization for white-LED nonlinearity in OFDM-based visible light communication systems“. Chinese Optics Letters 13, Nr. 7 (2015): 072302–72305. http://dx.doi.org/10.3788/col201513.072302.
Der volle Inhalt der QuelleKumar Orappanpara Soman, Sunish. „A tutorial on fiber Kerr nonlinearity effect and its compensation in optical communication systems“. Journal of Optics 23, Nr. 12 (22.11.2021): 123502. http://dx.doi.org/10.1088/2040-8986/ac362a.
Der volle Inhalt der QuelleSiuzdak, Jerzy. „Comparison of the Nonlinear Dynamic Pre- and Post-LED Equalization“. Sensors 22, Nr. 5 (24.02.2022): 1782. http://dx.doi.org/10.3390/s22051782.
Der volle Inhalt der QuelleScarano, Gaetano, Andrea Petroni, Mauro Biagi und Roberto Cusani. „Blind Fractionally Spaced Channel Equalization for Shallow Water PPM Digital Communications Links“. Sensors 19, Nr. 21 (23.10.2019): 4604. http://dx.doi.org/10.3390/s19214604.
Der volle Inhalt der QuelleAsif, Rameez, Rabeea Basir und Ramshah Ahmad. „Signal Processing Algorithms for Down-Stream Traffic in Next Generation 10 Gbit/s Fixed-Grid Passive Optical Networks“. Advances in OptoElectronics 2014 (22.06.2014): 1–4. http://dx.doi.org/10.1155/2014/296781.
Der volle Inhalt der QuelleMauda, R., und M. Pinchas. „16QAM Blind Equalization via Maximum Entropy Density Approximation Technique and Nonlinear Lagrange Multipliers“. Scientific World Journal 2014 (2014): 1–5. http://dx.doi.org/10.1155/2014/548714.
Der volle Inhalt der QuelleDissertationen zum Thema "Nonlinearity equalization"
Benammar, Bouchra. „Formes d'ondes avancées et traitements itératifs pour les canaux non linéaires satellites“. Phd thesis, Toulouse, INPT, 2014. http://oatao.univ-toulouse.fr/13567/1/Benammar.pdf.
Der volle Inhalt der QuelleShahkarami, Abtin. „Complexity reduction over bi-RNN-based Kerr nonlinearity equalization in dual-polarization fiber-optic communications via a CRNN-based approach“. Electronic Thesis or Diss., Institut polytechnique de Paris, 2022. http://www.theses.fr/2022IPPAT034.
Der volle Inhalt der QuelleThe impairments arising from the Kerr nonlinearity in optical fibers limit the achievable information rates in fiber-optic communication. Unlike linear effects, such as chromatic dispersion and polarization-mode dispersion, which can be compensated via relatively simple linear equalization at the receiver, the computational complexity of the conventional nonlinearity mitigation techniques, such as the digital backpropagation, can be substantial. Neural networks have recently attracted attention, in this context, for low-complexity nonlinearity mitigation in fiber-optic communications. This Ph.D. dissertation deals with investigating the recurrent neural networks to efficiently compensate for the nonlinear channel impairments in dual-polarization long-haul fiber-optic transmission. We present a hybrid convolutional recurrent neural network (CRNN) architecture, comprising a convolutional neural network (CNN) -based encoder followed by a recurrent layer working in tandem. The CNN-based encoder represents the shortterm channel memory arising from the chromatic dispersion efficiently, while transitioning the signal to a latent space with fewer relevant features. The subsequent recurrent layer is implemented in the form of a unidirectional vanilla RNN, responsible for capturing the long-range interactions neglected by the CNN encoder. We demonstrate that the proposed CRNN achieves the performance of the state-of-theart equalizers in optical fiber communication, with significantly lower computational complexity depending on the system model. Finally, the performance complexity trade-off is established for a number of models, including multi-layer fully-connected neural networks, CNNs, bidirectional recurrent neural networks, bidirectional long short-term memory (bi-LSTM), bidirectional gated recurrent units, convolutional bi-LSTM models, and the suggested hybrid model
Bartholomew, David Ray. „Design of a High Speed Mixed Signal CMOS Mutliplying Circuit“. Diss., CLICK HERE for online access, 2004. http://contentdm.lib.byu.edu/ETD/image/etd362.pdf.
Der volle Inhalt der QuelleBücher zum Thema "Nonlinearity equalization"
Gutiérrez, Alberto. Equalization and detection for digital communication over nonlinear bandlimited satellite communication channels. Las Cruces, N.M: New Mexico State University, 1995.
Den vollen Inhalt der Quelle findenAlberto, Gutiérrez. Equalization and detection for digital communication over nonlinear bandlimited satellite communication channels. Las Cruces, N.M: New Mexico State University, 1995.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Nonlinearity equalization"
Majumder, Saikat. „Wavelet Neural Networks and Equalization of Nonlinear Satellite Communication Channel“. In Applications of Artificial Neural Networks for Nonlinear Data, 207–26. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4042-8.ch009.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Nonlinearity equalization"
Riant, I., S. Borne und P. Sansonetti. „Asymetrical UV-Written Fibre Fabry-Perot for WDM Soliton Frequency-Guiding and Equalization“. In Photosensitivity and Quadratic Nonlinearity in Glass Waveguides. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/pqn.1995.sab.4.
Der volle Inhalt der QuelleZhang, Yang, und Saleem A. Kassam. „Blind equalization using coarse quantizer BSS nonlinearity“. In 2011 45th Annual Conference on Information Sciences and Systems (CISS 2011). IEEE, 2011. http://dx.doi.org/10.1109/ciss.2011.5766222.
Der volle Inhalt der QuelleKoike-Akino, T., Ye Wang, D. S. Millar, K. Kojima und K. Parsons. „Neural turbo equalization to mitigate fiber nonlinearity“. In 45th European Conference on Optical Communication (ECOC 2019). Institution of Engineering and Technology, 2019. http://dx.doi.org/10.1049/cp.2019.0803.
Der volle Inhalt der QuelleRosenkranz, Werner, und Johannes von Hoyningen-Huene. „Nonlinearity compensation and equalization in access networks“. In 2012 Opto-Electronics and Communications Conference (OECC). IEEE, 2012. http://dx.doi.org/10.1109/oecc.2012.6276521.
Der volle Inhalt der QuelleSilva, Edson Porto da, und Metodi Plamenov Yankov. „Adaptive Turbo Equalization of Probabilistically Shaped Constellations“. In Optical Fiber Communication Conference. Washington, D.C.: Optica Publishing Group, 2023. http://dx.doi.org/10.1364/ofc.2023.th2a.26.
Der volle Inhalt der QuelleXia, Chunmin, und Werner Rosenkranz. „Mitigation of Optical Intrachannel Nonlinearity Using Nonlinear Electrical Equalization“. In 2006 32nd European Conference on Optical Communications - (ECOC 2006). IEEE, 2006. http://dx.doi.org/10.1109/ecoc.2006.4801152.
Der volle Inhalt der QuelleLi, Xiangyu, Hanjie Chen, Shangbin Li, Qian Gao, Chen Gong und Zhengyuan Xu. „Volterra-based nonlinear equalization for nonlinearity mitigation in organic VLC“. In 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC). IEEE, 2017. http://dx.doi.org/10.1109/iwcmc.2017.7986356.
Der volle Inhalt der QuelleBeppu, S., K. Saito, N. Yoshikane und T. Tsuritani. „Fiber Nonlinearity Equalization with a Quantum-Enhanced Support Vector Machine“. In 2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC). IEEE, 2022. http://dx.doi.org/10.23919/oecc/psc53152.2022.9850116.
Der volle Inhalt der QuelleLei, Lei, Jianglong Lian, Ketong Zhang und Yutong Nan. „Post-Equalization Technique to Mitigate LED Nonlinearity in VLC Systems“. In 2023 IEEE International Conference on Image Processing and Computer Applications (ICIPCA). IEEE, 2023. http://dx.doi.org/10.1109/icipca59209.2023.10257998.
Der volle Inhalt der QuelleCho, Junho, und Son Thai Le. „Volterra Equalization to Compensate for Transceiver Nonlinearity: Performance and Pitfalls“. In Optical Fiber Communication Conference. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/ofc.2022.w2a.36.
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