In recent years, with the rapid development of internet businesses and the emergence of emerging industries, large-capacity and high-speed optical access network systems have been deployed and extensively studied. Passive optical network (PON) technology has become a cost-effective optical access network solution due to its natural advantages of low power consumption and flexible bandwidth access. However, because of the passive nature of PON systems, in the event of fiber faults, the fault points cannot actively report fault information to the central office, making real-time monitoring and fault location maintenance work difficult. Additionally, there is mutual interference between users in the centralized tree-shaped network architecture of PON systems, which makes the point-to-point fault monitoring scheme of traditional optical network systems unable to accurately locate the specific link and fault. Therefore, studying effective PON system monitoring plans has also become one of the focuses of future PON system research. PON system fault detection methods, such as improved schemes using wavelength-tunable OTDR, Brillouin OTDR, and optical feedback with chaotic lasers, require adding difficult-to-install and/or expensive equipment to the system, which increases the system cost to some extent. Alternatively, although existing optical encoding schemes have good scalability and low upgrade cost, they have not considered the problem of system performance degradation caused by interference between different users. In this article, a PON monitoring system based on multi-wavelength grouping is proposed. By assigning users prone to interference to different groups and deploying corresponding reflecting Bragg gratings at the end of the link, different wavelengths are used to detect and distinguish between user groups, thereby reducing the probability of interference. We hope that our scheme and model can provide useful assistance for the design of PON fault monitoring systems.

A user distribution model for PON is developed to calculate the interference probability function of users, and an optimization approach for multi-wavelength packet scheduling is mathematically formulated. The simulation results of this model have verified the feasibility of multi-wavelength packet scheduling as a means of reducing inter-user interference. The system performance is simulated by MATLAB based on the received signal power and the function of various noise components, and appropriate transmission power and pulse width parameters are obtained for different numbers of users and distribution ranges. The feasibility of a fault monitoring system for PONs based on multi-wavelength packet scheduling is simulated by OptiSystem and verified.

Figure 3 shows the probability of interference occurrence under the multi-wavelength grouping scenario, where it can be effectively avoided when the number of equally spaced users is less than the wavelength number. The research results on the pulse width of the system show that the signal-to-noise ratio (SNR) has ideal values when the pulse width range is -10≤lgT_C ≤-8 (Fig. 4). When the pulse width T_C ≥10^-7 s, the signal-to-interference ratio (SIR) gradually approaches a lower boundary value (Fig. 6). The pulse width cannot be too narrow because reducing the pulse width requires a higher accuracy requirement for the system. Therefore, we suggest setting the pulse width to -9≤ lgT_C ≤-8 in a PON fault monitoring system based on multi-wavelength grouping. Figure 5 shows that the SNR improves linearly with the transmitted power. It is found that when the number of users increases, the SNR exhibits a decreasing trend (Fig. 5). This is because an increase in the number of users within the same interference range causes an increase in both beat noise and shot noise, leading to a decrease in SNR. Additionally, when the maximum user spacing l_e = 1 km and the number of users K increases from 64 to 128, the increase in interference causes SIR to decrease from 40.6 dB to 34.9 dB. This is because the decrease in user separation distance leads to an increase in interference, which in turn leads to a decrease in SIR. A PON fault monitoring system based on multi-wavelength grouping, with wavelengths of m=4 and users of K=16, is constructed in OptiSystem software, and at a transmitted power of P_s=4 dBm and pulse width T_C=10^-8 s, the identification accuracy for 3 dB pulse width corresponds to 2 m (Fig. 7).

A PON fault monitoring system based on multi-wavelength grouping is proposed. First, the interference caused by user distribution in PON systems is analyzed, and the probability of user distribution is derived. Subsequently, the optimal solution method for multi-wavelength grouping is mathematically modeled, and the theoretical impact of this grouping method on interference probability is obtained. It is proved that the multi-wavelength grouping method can effectively reduce the interference probability between users. In addition, the anti-interference performance of the PON fault monitoring system based on multi-wavelength grouping is investigated. The influence of different system parameters on SNR and SIR is simulated. For different user numbers and distribution ranges, selecting appropriate pulse widths and output powers can effectively improve system performance. It is found that when the pulse width is set to -9≤lgT_C ≤ -8, it can effectively suppress noise interference in the signal, thereby improving SNR, while also effectively reducing interference between users and ensuring that SIR remains within an acceptable range. Finally, by using the OptiSystem software, the system is simulated under conditions of wavelength number m=4, user number K=16, transmit power P_s =4 dBm, and pulse width T_C=10^-8 s. The simulation results show that the recognition accuracy corresponding to a pulse width of 3 dB is 2 m, and the results in the detection signal recognition module are consistent with the set parameters, verifying the effectiveness of the system. This work provides guidance for the design and parameter selection of PON fault monitoring systems.