Unified Performance Evaluation of Optical Fiber Nonlinear Compensation Technologies

Abstract

Driven by emerging high-bandwidth applications including 5G and cloud computing, the continual explosion of data traffic threatens to push optical fiber transmission systems up against their capacity limits. In addition, coherent optical communication with advanced digital signal processing (DSP) demonstrates almost perfect linear impairment compensation. As a result, the Kerr nonlinear effect has become the core physical bottleneck limiting the performance of long-haul and high-capacity optical transmission systems. Mitigating nonlinear impairments effectively in order to overcome the nonlinear Shannon limit has become a central challenge of optical communications. This paper systematically reviews four typical DSP-based deterministic physical model-driven nonlinear compensation schemes. These technologies are digital back-propagation (DBP), Volterra series nonlinear equalizer (VSNE), nonlinear Fourier transform (NFT), and phase-conjugated twin waves (PCTW). This review establishes a systematic evaluation framework covering Q-factor gain, computational complexity, and spectral efficiency. In addition, a deep analysis of the performance-complexity trade-offs for each technology is performed in this paper. The extensive comparative analysis illustrates that there are intrinsic trade-offs among computational complexity, spectral efficiency, and compensation performance for all current methods. Beyond that, intelligent compensation schemes combining physical models and data-driven methods like deep learning will be an important trend for resolving these constraints.