• Developed an index model optimizing OFH/PC code selection for efficient fault detection. • Proposed a WSRT-based method for feature extraction from time-domain amplitude data. • Addressed index mismatches via a dynamic optimization algorithm. • Achieved integrated fault detection and localization without additional components. We present an optimization-based OFH/PC PON monitoring technique that addresses the challenge of fault detection in large-scale PONs by simplifying the final recognition process while enabling large monitoring capacity. First, in high-density networks with 128 end-users, a 35ns monitoring pulse is shown to reduce the multi-customer interference probability by 36% compared with both PC and OFH/PC techniques. Second, an optimal cost-performance index model is developed to determine the best OFH/PC code combinations, thereby improving fault detection efficiency. Third, we introduce a novel feature extraction method that applies the Wilcoxon signed-rank test to time-domain trace amplitude data at fault points. By exploiting differential features between signal and non-signal regions, this method enables precise fault localization without the need for OTDR. The proposed scheme is further enhanced with a dynamic index matching optimization algorithm to address inconsistencies between the starting index intervals of detected signal regions and the predefined integer period intervals after sampling. Realistic simulations validate the feasibility and effectiveness of the framework. The results show that the localization error does not exceed 1.4m, the localization time is less than 3s, and the overall scheme reliably detects faults while requiring no additional components in existing PON monitoring infrastructure, making it highly suitable for the cost-sensitive PON market.
Zhang et al. (Sun,) studied this question.