V. Bajaj
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10 records found
1
The Kerr nonlinear effects add phase shifts to the signal, which are dependent on its instantaneous power. These phase distortions occur simultaneously with the dispersion effect of the fiber, which spreads signal pulses in time. The interplay is complicated and makes compensation of distortions challenging. The nonlinear Fourier transform(NFT), which offers immunity from the distortions of the Kerr effect, received great interest in recent years. The lossless nonlinear Schrödinger equation (NLSE), which models signal propagation in an ideal lossless optical fiber, belongs to a class of nonlinear partial differential equations known as integrable equations. These integrable equations can be solved exactly by NFT. Similar to the Fourier transform that translates a linear dispersive propagation in the time domain into phase delays in the signal spectrum, the NFT translates the nonlinear evolution of the signal governed by the lossless NLSE into trivial multiplications in the nonlinear Fourier spectrum of the signal. The NFT is exact for lossless fiber channels. In the presence of loss, the integrability property is violated. In lossy propagation, signal power reduces as it propagates. This in turn reduces the strength of the nonlinear effects along the length of the fiber. As practical fibers are lossy, the path-average approximation is often used to apply NFT on lossy fiber channels. In this approximation, the variation in the Kerr nonlinear effects due to the reduction in signal power is accounted as the variations in the Kerr-nonlinearity parameter of the fiber. Then, by approximating the varying Kerr-nonlinearity parameter with its average value over a span, a lossless fiber model is obtained. This approximation has errors associated with it which sacrifices the performance. We developed a NFT-based transmission system that is exact even in the presence of fiber loss. The proposed design eliminates errors due to loss, thus improving performance over the design that uses path-average approximation… ...
The Kerr nonlinear effects add phase shifts to the signal, which are dependent on its instantaneous power. These phase distortions occur simultaneously with the dispersion effect of the fiber, which spreads signal pulses in time. The interplay is complicated and makes compensation of distortions challenging. The nonlinear Fourier transform(NFT), which offers immunity from the distortions of the Kerr effect, received great interest in recent years. The lossless nonlinear Schrödinger equation (NLSE), which models signal propagation in an ideal lossless optical fiber, belongs to a class of nonlinear partial differential equations known as integrable equations. These integrable equations can be solved exactly by NFT. Similar to the Fourier transform that translates a linear dispersive propagation in the time domain into phase delays in the signal spectrum, the NFT translates the nonlinear evolution of the signal governed by the lossless NLSE into trivial multiplications in the nonlinear Fourier spectrum of the signal. The NFT is exact for lossless fiber channels. In the presence of loss, the integrability property is violated. In lossy propagation, signal power reduces as it propagates. This in turn reduces the strength of the nonlinear effects along the length of the fiber. As practical fibers are lossy, the path-average approximation is often used to apply NFT on lossy fiber channels. In this approximation, the variation in the Kerr nonlinear effects due to the reduction in signal power is accounted as the variations in the Kerr-nonlinearity parameter of the fiber. Then, by approximating the varying Kerr-nonlinearity parameter with its average value over a span, a lossless fiber model is obtained. This approximation has errors associated with it which sacrifices the performance. We developed a NFT-based transmission system that is exact even in the presence of fiber loss. The proposed design eliminates errors due to loss, thus improving performance over the design that uses path-average approximation…
While probabilistic constellation shaping (PCS) enables rate and reach adaption with finer granularity [1] (Cho and Winzer, 2009), it imposes signal processing challenges at the receiver. Since the distribution of PCS-quadrature amplitude modulation (QAM) signals tends to be Gaussian, conventional blind polarization demultiplexing algorithms are not suitable for them [2] (Johnson et al., 1998). It is known that independently and identically distributed (iid) Gaussian signals, when mixed, cannot be recovered/separated from their mixture. For PCS-QAM signals, there are algorithms such as [3] and [4] Dris et al. (2019) and Athuraliya et al. (2004) which are designed by extending conventional blind algorithms used for uniform QAM signals. In these algorithms, an initialization point is obtained by processing only a part of the mixed signal, which have non-Gaussian statistics. In this article, we propose an alternative method wherein we add temporal correlations at the transmitter, which are subsequently exploited at the receiver in order to separate the polarizations. We will refer to the proposed method as frequency domain (FD) joint diagonalization (JD) probability aware-multi modulus algorithm (pr-MMA), and it is suited to channels with moderate polarization mode dispersion (PMD) effects. Furthermore, we extend our previously proposed JD-MMA [5] (Bajaj et al., 2022) by replacing the standard MMA with a pr-MMA, improving its performance. Both FDJD-pr-MMA and JD-pr-MMA are evaluated for a diverse range of PCS (entropy $\mathcal {H}$) of 64-QAM over a first-order PMD channel that is simulated in a proof-of-concept setup. A MMA initialized with a memoryless constant modulus algorithm (CMA) is used as a benchmark. We show that at a differential group delay (DGD) of 10% of symbol period T$_{\text{symb}}$ and 18 dB SNR/pol., JD-pr-MMA successfully demultiplexes the PCS signals, while CMA-MMA fails drastically. Furthermore, we demonstrate that the newly proposed FDJD-pr-MMA is robust against moderate PMD effects by evaluating it over a DGD of up to 40% of T$_{\text{symb}}$. Our results show that the proposed FDJD-pr-MMA successfully equalizes PMD channels with a DGD up to 20% of T$_{\text{symb}}$.
We propose a novel method for blind polarization-demultiplexing of probabilistically shaped signals for coherent receivers. The method is capable of separating signals with (quasi) Gaussian distributions by exploiting temporal correlations added to the transmit signals. The proposed method is evaluated in challenging mixing scenarios.
High-symbol-rate coherent optical transceivers suffer more from the critical responses of transceiver components at high frequency, especially when applying a higher order modulation format. Recently, we proposed in [20] a neural network (NN)-based digital pre-distortion (DPD) technique trained to mitigate the transceiver response of a 128~GBaud optical coherent transmission system. In this paper, we further detail this work and assess the NN-based DPD by training it using either a direct learning architecture (DLA) or an indirect learning architecture (ILA), and compare performance against a Volterra series-based DPD and a linear DPD. Furthermore, we willfully increase the transmitter nonlinearity and compare the performance of the three DPDs considered. The proposed NN-based DPD trained using DLA performs the best among the three contenders, providing more than 1~dB signal-to-noise ratio (SNR) gains for uniform 64-quadrature amplitude modulation (QAM) and PCS-256-QAM signals at the output of a conventional coherent receiver DSP. Finally, the NN-based DPD enables achieving a record 1.61~Tb/s net rate transmission on a single channel after 80~km of standard single mode fiber (SSMF).
We demonstrate a record 54.5 Tb/s WDM transmission at 11.35 bit/s/Hz over 48 km of field-deployed SMF connecting business and academic parks enabled by a novel joint I-Q Neural Network-based transmitter digital pre-distortion technique.
We propose an efficient neural-network-based equalization jointly compensating fiber and transceiver nonlinearities for high-symbol-rate coherent short-reach links. Providing about 0.9 dB extra SNR gain, it allows achieving experimentally the record single-channel 1.48 Tbps net rate over 240 km G.652 fiber.