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Next-generation coherent optical systems are geared to employ high-speed digital-to-analog converters (DAC), allowing for digital preprocessing of the signal and flexible optical transport networks. However, one of the major obstacles in such architectures is the limited resolution (less than 5.5 effective bits) and -3 dB bandwidth of commercial DACs, typically limited to half of the currently commercial baud rates, and even relatively reduced in case of higher baud rate transponders (400 Gb/s and 1 Tb/s). In this paper, we propose a simple digital preemphasis (DPE) algorithm to compensate for DAC-induced signal distortions, and exhaustively investigate the impact of DAC specifications on system performance, both with and without DPE. As an outcome, performance improvements are established across various DAC hardware requirements (effective number of bits and bandwidth) and channel baud rates, for m-state quadrature amplitude modulation (QAM) formats. In particular, we show that lower order modulation formats are least affected by DAC limitations, however, they benefit the most from DPE in extremely challenging hardware conditions. On the contrary, higher order formats are severely limited by DAC distortions, and moderately benefit from DPE across a wide range of DAC specifications. Moreover, effective number of bit requirements are established for m-state QAM, assuming low and high baud rate transmission regimes. Finally, we discuss the application scenarios for the proposed DPE in next-generation terabit transmission systems, and establish maximum transportable baud rates, which are shown to be used toward increasing channel baud rates to reduce terabit subcarrier count or toward increasing forward error correction (FEC) overheads to reduce the pre-FEC bit error rate threshold. Maximum baud rates after DPE are summarized here for polarization multiplexed BPSK, QPSK, 8QAM, and 16QAM, assuming two DACs: Current commercial DACs (5.5 effective bits, 16 GHz bandwidth) 57, 54, 51, and 48 Gbaud, respectively. Next-generation DACs (7 effective bits, 22 GHz bandwidth): 62, 61, 60, and 58 Gbaud, respectively.

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Nonlinear distortion has always been a challenge for optical communication due to the nonlinear transfer characteristics of the fiber itself. The next frontier for optical communication is a second type of nonlinearities, which results from optical and electrical components. They become the dominant nonlinearity for shorter reaches. The highest data rates cannot be achieved without effective compensation. A classical countermeasure is receiver-side equalization of nonlinear impairments and memory effects using Volterra series. However, such Volterra equalizers are architecturally complex and their parametrization can be numerical unstable. This contribution proposes an alternative nonlinear equalizer architecture based on machine learning. Its performance is evaluated experimentally on coherent 88 Gbaud dual polarization 16QAM 600 Gb/s back-to-back measurements. The proposed equalizers outperform Volterra and memory polynomial Volterra equalizers up to 6th orders at a target bit-error rate (BER) of 10 − 2 by 0.5 dB and 0.8 dB in optical signal-to-noise ratio (OSNR), respectively.

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