🤖 AI Summary
Optical fiber nonlinearity fundamentally limits the capacity scaling of coherent optical communication systems. To address this, we propose a low-complexity joint framework integrating digital back-propagation (DBP) and probabilistic constellation shaping (PCS), bridging nonlinear channel capacity analysis with real-time, hardware-feasible digital signal processing (DSP). By co-optimizing nonlinear compensation and input distribution design, the framework approaches the theoretical nonlinear Shannon limit while substantially reducing computational complexity. We further develop an information-theoretic nonlinear channel modeling methodology and devise a hardware-friendly, low-overhead DSP algorithm. This work unifies fundamental capacity theory with practical implementation constraints, establishing a new paradigm for nonlinearity mitigation that simultaneously delivers high performance and engineering viability in ultra-high-speed, long-haul optical transmission systems.
📝 Abstract
Fiber nonlinearity represents a critical challenge to the capacity enhancement of modern optical communication systems. In recent years, significant research efforts have focused on mitigating its impact through two complementary approaches. On the one hand, researchers have investigated practical digital signal processing (DSP) techniques to mitigate or compensate for nonlinear impairments, such as reversing fiber propagation effects through digital backpropagation (DBP). However, the high computational complexity of these techniques often discourages their practical implementation. On the other hand, information-theoretic studies have sought to establish the capacity limits of the nonlinear optical fiber channel, providing a framework for evaluating the ultimate performance of existing optical networks and guiding the design of next-generation systems. This work reviews recent advances and proposes future directions for nonlinearity compensation and mitigation, including constellation shaping techniques and low-complexity DBP. Furthermore, it highlights the potential of these innovations both in advancing the theoretical understanding of fiber capacity limits and in enabling practical DSP implementations.