Probabilistic shaping and neural network-based optimization for nonlinear frequency division multiplexing system

2021 ◽  
Author(s):  
Jiacheng Wei ◽  
Lixia Xi ◽  
XuLun Zhang ◽  
jiayun deng ◽  
Shucheng Du ◽  
...  
Keyword(s):  
Neural Network ◽  
Frequency Division Multiplexing ◽  
Nonlinear Frequency ◽  
Frequency Division

scholarly journals Experimental Demonstration of Nonlinear Frequency Division Multiplexing Transmission With Neural Network Receiver

2020 ◽  
Vol 38 (23) ◽  
pp. 6465-6473
Author(s):  
Simone Gaiarin ◽  
Francesco Da Ros ◽  
Nicola De Renzis ◽  
Rasmus T. Jones ◽  
Darko Zibar
Keyword(s):  
Neural Network ◽  
Frequency Division Multiplexing ◽  
Nonlinear Frequency ◽  
Frequency Division ◽  
Experimental Demonstration

scholarly journals Robust neural network receiver for multiple-eigenvalue modulated nonlinear frequency division multiplexing system

2020 ◽  
Vol 28 (12) ◽  
pp. 18304 ◽  
Author(s):  
Yue Wu ◽  
Lixia Xi ◽  
Xulun Zhang ◽  
Zibo Zheng ◽  
Jiacheng Wei ◽  
...  
Keyword(s):  
Neural Network ◽  
Multiple Eigenvalue ◽  
Frequency Division Multiplexing ◽  
Nonlinear Frequency ◽  
Frequency Division

scholarly journals Deep neural network for orthogonal frequency division multiplexing systems without cyclic prefix transmission

2018 ◽  
Vol 189 ◽  
pp. 04016
Author(s):  
Viet-Hung Nguyen ◽  
Minh-Tuan Nguyen ◽  
Yong-Hwa Kim
Keyword(s):  
Neural Network ◽  
Orthogonal Frequency Division Multiplexing ◽  
Deep Neural Network ◽  
Cyclic Prefix ◽  
Wireless Transmission ◽  
Frequency Division Multiplexing ◽  
Frequency Division ◽  
Ofdm System ◽  
New Approach ◽  
Multiplexing Systems

Orthogonal frequency division multiplexing (OFDM) is widely used in wired or wireless transmission systems. In the structure of OFDM, a cycle prefix (CP) has been exploited to avoid the effects of inter-symbol interference (ISI) and inter-carrier interference (ICI). This paper proposes a new approach to transmit the signals without CP transmission. Using the deep neural network, the proposed OFDM system transmits data without the CP. Simulation results show that the proposed scheme can estimate the CP at the receiver and overcome the effect of ISI.

scholarly journals Mitigating the Impact of Noise on Nonlinear Frequency Division Multiplexing

2020 ◽  
Vol 10 (24) ◽  
pp. 9099
Author(s):  
Stella Civelli ◽  
Enrico Forestieri ◽  
Marco Secondini
Keyword(s):  
Dispersion Compensation ◽  
Kerr Nonlinearity ◽  
Optical Fiber Communication ◽  
Frequency Division Multiplexing ◽  
Nonlinear Frequency ◽  
Frequency Division ◽  
Fiber Communication ◽  
Range Rate ◽  
Conventional Systems ◽  
The Impact

In the past years, nonlinear frequency division multiplexing (NFDM) has been investigated as a potentially revolutionary technique for nonlinear optical fiber communication. However, while NFDM is able to exploit the Kerr nonlinearity, its performance lags behind that of conventional systems. In this work, we first highlight that current implementations of NFDM are strongly suboptimal, and, consequently, oversensitive to noise: the modulation does not ensure a large minimum distance between waveforms, while the detection is not tailored to the statistics of noise. Next, we discuss improved detections strategies and modulation techniques, proposing some effective approaches able to improve NFDM. Different flavors of NFDM are compared through simulations, showing that (i) the NFDM performance can be significantly improved by employing more effective detection strategies, with a 5.6 dB gain in Q-factor obtained with the best strategy compared to the standard strategy; (ii) an additional gain of 2.7 dB is obtained by means of a simple power-tilt modulation strategy, bringing the total gain with respect to standard NFDM to 8.3 dB; and (iii) under some parameters range (rate efficiency η≤30%), the combination of improved modulation and detection allows NFDM to outperform conventional systems using electronic dispersion compensation.

scholarly journals Spectral function modulation based on nonlinear frequency division multiplexing

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Guangqiang He ◽  
Luning Wang ◽  
Chenyang Li ◽  
Siyu Liu ◽  
Weisheng Hu
Keyword(s):  
Spectral Function ◽  
Frequency Division Multiplexing ◽  
Nonlinear Frequency ◽  
Frequency Division
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