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The effect of Kerr-induced optical fiber nonlinearities in C-band (5 THz) EDFA and C+L-band (12.5 THz) Raman-amplified optical communication systems has been studied considering the impact of third-order fiber dispersion. The performance of digital nonlinearity compensation with single channel and 250-GHz bandwidth in both EDFA and Raman amplified systems has been investigated, respectively. The achievable information rates (AIRs) and optimum code rates in each individual transmission channel have been evaluated for the DP-64QAM, the DP-256QAM and the DP-1024QAM modulation formats, both with and without the use of the probabilistic shaping technique. It is found that, for all considered modulation formats, the signal-to-noise ratios, AIRs and code rates exhibit significantly asymmetric behavior about the central channel due to the presence of the third-order dispersion. This provides a new insight that the forward error correction schemes have to be optimized asymmetrically, on a per-channel basis, to maximize the overall throughput.
An eight element, compact Ultra Wideband-Multiple Input Multiple Output (UWB-MIMO) antenna capable of providing high data rates for future Fifth Generation (5G) terminal equipments along with the provision of necessary bandwidth for Third Generation
Free-space optical (FSO) communications has the potential to revolutionize wireless communications due to its advantages of inherent security, high-directionality, high available bandwidth and small physical footprint. The effects of atmospheric turb
Intelligent reflecting surface (IRS) is considered as an enabling technology for future wireless communication systems since it can intelligently change the wireless environment to improve the communication performance. In this paper, an IRS-enhanced
Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance optical fiber communication. Digital back-propagation (DBP) is a primary methodology to mitigate both linear and nonlinear impairments by solving the inve
We propose a new machine-learning approach for fiber-optic communication systems whose signal propagation is governed by the nonlinear Schrodinger equation (NLSE). Our main observation is that the popular split-step method (SSM) for numerically solvi