Comparative Analysis of Digital Signal Processing Techniques for Chromatic Dispersion Compensation in High-Speed Optical Communication Systems

Abstract

This paper addresses the critical gap in comparative analysis of digital signal processing techniques for chromatic dispersion compensation in high-speed optical communication systems. Previous studies have demonstrated individual technique effectiveness but lacked integrated assessment frameworks necessary for informed implementation decisions, particularly regarding the trade-off between performance and computational efficiency. The research aimed to design and simulate an optimized Digital Signal Processing (DSP) model for chromatic dispersion compensation using MATLAB, with specific objectives to develop a comprehensive simulation framework, implement multiple compensation techniques, evaluate performance across critical metrics, and compare proposed approaches with existing methods. MATLAB-based simulation using 16-QAM modulation at 25 G Baud with Root Raised Cosine pulse shaping, incorporating realistic fibre characteristics with 17 ps/(nm·km) chromatic dispersion was employed. Four compensation techniques were evaluated: baseline uncompensated transmission, Finite Impulse Response (FIR) standard filtering, FIR weight-optimized filtering, and frequency domain standard compensation. Performance assessment encompassed six transmission distances (100-1500 km) and six OSNR conditions (10-30 dB), generating comprehensive datasets for statistical analysis. The findings revealed that whilst all compensation techniques achieved comparable bit error rate performance clustering around 5×10⁻¹, significant differences emerged in computational complexity. The frequency domain standard technique demonstrated superior efficiency requiring only 9.83×10⁵ operations compared to 4.19×10⁶ operations for FIR standard filtering, representing a 76% reduction in processing requirements. Statistical analysis confirmed that performance differences were not statistically significant at the 95% confidence level, establishing computational efficiency as the primary differentiating factor.