Remote Real-Time Optical Layers Performance Monitoring Using a Modern FPMT Technique Integrated with an EDFA Optical Amplifier

Abstract

Fiber performance monitoring using modern online technologies in the next generation of intelligent optical networks allows for identifying the source of the degeneration and putting in protective steps to increase remote optical network stability & reliability. In this paper, the performance of the fiber performance monitoring tool (FPMT) technique was improved by integrating it with optical amplifier boards. In this regard, the improved technique detects optical layer events and all fiber soft and hard failures at the online remote rather than disrupting the data flow with a measurement accuracy for defect location of up to ~99.9%, small tolerance of up to ~1 m, the longest distance to detecting optical line defects of up to ~300km, and enhanced power budget for the system with optimum insertion-loss of up to ~0.0 dB. The proposed integration method provides better results with an excellent and efficient solution at fault location measurement & detection in real-time with good financial implications of the technique. The competitiveness of the improved technique over the actual optical networks has been successfully confirmed through application to Huawei labs infrastructure nodes and displayed experimental simulation results.

1. Introduction

In recent years, optical network systems have doubled their information rate using a new multiplexing technique known as the Orbital Angular Momentum (OAM) of light, which gives signal carriers additional freedom [1]. By utilizing new, sophisticated modulation formats and various access strategies in the network layers, particularly the physical layer, the development of optical networks, such as Passive Optical Networks (PONs), has produced a rise in the quantity of the data rate (PL) [2,3,4,5]. At the same time, fiber optic data transmission lines are considered one of the most important methods currently used, as they sustain transmitting high data rates for extended transmission distances of up to several gigabits per second [6,7,8,9,10].

Optical Performance Monitoring (OPM) is considered a necessity over an optical network to enable sensibility of traffic line status and attain outstanding Quality-of-Service (QoS) [11]. OPM is also a crucial tool in these networks as it improves network controllability and provides flexibility [12,13]. OPM eliminates optical network downtime and data interruption and increases reliability and availability. The predicted soft and hard failures enable the passive optical network to quickly detect and solve these impairments, which leads to optimizing network resources [3,14].

Therefore, real-time OPM is imperative in the optical network. With that consideration, in the next optical network generation, the optical transmission lines must be created with OPM units. To detect mistakes that happen via optical cables, these units must be affordable and accurate, have low insertion loss devices, be simple to configure, and quick to monitor performance. To protect an optical network against hard and soft failures in real-time, every channel in the traffic plane should have its performance continuously measured. If impairment is found, the control plane must be informed so that the traffic in the optical network may be reconfigured [15].

The Fiber Performance Monitoring Tool (FPMT) technique is one modern method of monitoring fiber line performance failure remotely in real-time without disturbing the traffic. In this manuscript, the authors rely on previous work [16]. In this paper, we use the modern FPMT technique integrated with Optical Amplifiers (OAs) to monitor the optical layer and hard and soft fiber line failure performance, which leads to better results.

The network layer performance monitoring has been carefully analyzed in many literary works. We think the current Machine Learning (ML) applications focus on traffic transmission in the network layer to focus on network layer failure (physical layer). This research [17] concentrates on PL defects.

In this work, attention has been paid to studying the Hard Failures (HF) as fiber cuts (break) and Soft Failures (SF) as fiber bending, fiber scratches, fiber contamination, fiber burning, insertion loss on the connector, etc. [18,19,20]. We consider the literature studies to be poor and few (detecting failures in the optical layer) which will be clarified and presented in the next section

1.1. Review of Related Research and Other Literature

Looking back to earlier studies, we observe that the processes of OPM are separated into monitoring for (offline) detection and performance and monitoring for (online) detection and performance. The literature depends on real-time detection in two ways: by detecting different events in network layers, especially the physical layer (PL), or by detecting and locating HF and SF over the optical layer. The optical performance monitoring classification is shown in Figure 1.

The online and offline detection methods are varied, and different research will be reviewed and referenced.

Auwalu et al. [17] presented plentiful solutions for performance monitoring the optical line in Passive Optical Networks using an Optical Time Domain Reflectometer (OTDR), considered most prevalent in characterizing fiber lines from point to point. However, it has been shown that the OTDR method is poor for characterizing network failures as there are many back-reflected signals that the OTDR detector cannot specify all at once.

Due to the maintenance and operating costs that would considerably increase the system’s cost, the old OTDR process is not financially feasible since it disturbs and extends the time required to fix the network system and disrupts traffic flow during the detection period [21,22].

The OTDR approach is only employed to identify and describe defects that have already happened, not to predict defects [23].

The OTDR depends on offline monitoring and performance detection mechanisms and needs a team on the site of the problem [23,24].

Developing an optical communication system and digital signal processing application based on Φ phase-sensitive OTDR (Φ OTDR) should be prioritized. The new obstruction for OTDR based-technologies is developing a more effective, stable, and dependable system than that now employed for engineering programs & applications presented in [25,26].

In [26,27,28,29], the authors explain that the Φ-OTDR system was developed for industrial communities, such as gas and oil pipelines, submarine cables, rails, airport security, perimeter security systems, and underground cable monitoring systems, with good results in real-time monitoring.

Real-time defect detection and prediction of any changes in measurement has been suggested by authors by monitoring important signal parameters, such as Modulation-Formats (MF), Mode-Coupling (MC), Polarization, Dispersion, Error Correct Codes, and Signal-to-Noise-Ratio (SNR) [30,31,32,33].

Other Refs. [34,35,36] also suggest physical layer monitoring for online performance states of fiber networks to investigate layer and traffic conditions, then providing the controller with the feedback to make defect predictions.

The literature on online performance monitoring uses Fourier Spectrum Analysis (FSA), a methodology created for OSNR online monitoring presented in [37].

Bakar et al. [38] describe how the Fiber-Break-Monitoring-System (FBMS) was created to identify optical breaks in real-time, with low cost and acceptable location measurement accuracy.

Liang et al. [39] say fault detection depends on digital signal processing (DSP) algorithm detection, such as Received-Optical-Power (ROP), Bit Error Rate (BER), pre-Bit Error Correction (pre-FEC), filter weights, and optical spectra.

Liang et al. [40] also presented fault detection that depends on digital spectra in Soft Failure Identification (SFI) and Soft Failure Detection (SFD).

The defect localization and detection rely on using the same transmitter module, performing in sequences, using algorithms and one or more fibers. Each final user is given a set of Bragg gratings with a unique reflection wavelength for failure detection [41].

In [42,43], the authors explain that failure detection is dependent on an ability to identify the reflection spectrum produced by every individual fiber Bragg grating.

Atef et al. [16] proposed a new technique to detect fiber failures remotely online, without interrupting traffic, depending on the pattern shape of the reflected test signal generated by the FPMT technique.

1.2. Aim of the Paper

There is an urgent need for a quick fix in real-time to inspect and monitor optical pass defects remotely instead of stopping traffic flow with an economical cost and get beyond the constraints of novel online techniques and drawbacks of the conventional OTDR approach. Hence, we can optimize the optical transmission network and reduce downtime [17,44,45,46].

In this thesis, the authors’ suggested framework is to implement a new technology based on remote real-time optical layer performance monitoring and failure detection using a modern FPMT technique integrated with an Erbium-Doped Fiber Amplifier (EDFA) OA that detects, locates, and estimates all HF and SF through the Optical Layer (OL).

The proposed integration method provides better results and an effective method for locating and detecting defects in real-time with good financial implications. It also enhanced the power budget for the system with no insertion loss, detected fiber lines over extremely long distances, showed high measurement precision for fiber defect detection, and detected all OL defect types over single-mode fiber (SMF) and Passive Optical Networks (PONs).

Other research operates in real-time performance monitoring based only on physical layer parameter measurements [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47].

1.3. Organization of the Paper

The residuum of this manuscript is organized in the manner shown. In Section 2, the modern FPMT technique working principle is introduced. In Section 3, optical amplifier types used in the DWDM system are displayed. In Section 4, the EDFA optical amplifier board before and after integration with a modern FPMT detection circuit is presented. In Section 5, the maximum distance equation for performance monitoring using an EDFA board is proved. In Section 6, the final results and the experimental investigations are presented. The evaluation of different techniques and final practical conclusions are discussed in Section 7. Finally, recommendations are provided in Section 8.

2. Modern FPMT Technique Working Principle

A detection circuit called FPMT is used to produce test signals with predefined specifications and transmit them together with service signal flows to detect any SF or HF through OL remotely and online [16]. The FPMT circuit consists of five main stages to generate test signals [16]. The modern FPMT technique detection circuit diagram is clarified in Figure 2.

The methodology of detecting failures in the optical layer involves each failure having a certain pattern shape recognized by monitoring and analyzing the reflected test signal. The test signals were produced by modern FPMT according to a specification shown in

Table 1. The test signals specification.

Parameter	Specification
Network	DWDM
Signal generate by	Modern-FPMT Technique
Signal transmit by	Integrate at EDFA-OA
Signal shape	Random predefined Binary-Bits
Signal pulse width	20 × 103 Nano-seconds
Signals rate	2 Mbps
Signal wavelength	1480 nm or 1490 nm
Attenuation	0.22 dB/km
Fiber-standard	SFM-G.652
Max. detected distance	300 km derived in Section 5

.

3. Optical Amplifiers Used to Transmitting Test Signals

The OAs are used to increase signal strength and enable long-distance transmission. Two types of amplifiers can be used with the Dense Wavelength Division Multiplexing (DWDM) system, the EDFA or Raman amplifiers.

Table 2. Comparison between the features of EDFA and Raman amplifiers.

Features	EDFA	Raman
Working principle	It uses stimulated radiation of EDF fibers to amplify optical signals and requires-doped optical fiber.	It uses stimulated Raman scattering to amplify optical signals and does not require doped-optical fiber.
Pump power	26 dBm	30 dBm
Amplification band	1525–1565 nm 1570–1610 nm	All wavelengths
Noise figure	5 dB	5 dB
Gain	40 dB	25 dB
Financial cost factor	Low cost	High cost

shows the main difference and features between the EDFA and Raman amplifiers [48,49,50,51,52,53].

In this research, we use an EDFA optical amplifier to improve the performance of the FPMT technique by integrating it with EDFA optical amplifier boards. In this regard, the improved technique detects optical layer events and all fiber soft and hard failures, as will be presented in Section 5.

EDFA is an optical amplifier widely used in PON and DWDM systems. It has a low financial cost and a low noise figure leading to low insertion loss; the EDFA optical amplifier has no insertion loss, is an active element [50,51,52], and its gain can be bigger than 20 dBm or 40 dB, helping to detect failures at more distance. In this research, we describe how to transmit test signals generated by modern FPMT through integration with an EDFA board instead of [16] using the optical supervisory channel (OSC) board. The EDFA board optical amplifier specification parameters are shown in

Table 3. The parameter of the EDFA optical amplifier board.

Parameter	Specification
Cabinet Name	OSN_9800
Optical Amplifier Type	Optical Booster unit (OBU) in TX Optical Amplifier unit (OAU) in Rx
Wavelength	1525 nm or 1565 nm [49,50]
Frequency	191 THz to 196 THz [49,50]
Insertion Loss	0 dB (active element) Refs. [50,51,52]
Receiver Sensitivity	−48 dBm [48]
Max. Transmit Power	26 dBm [48]
Max. Attenuation Loss	−67 dBm in Section 5
Max. Transmit Distance	300 km in Section 5

.

4. Modern FPMT Detection Circuit after and before Integration with EDFA Optical Amplifier Board

The block diagram of the EDFA board before combining it with the modern FPMT detection circuit is shown in Figure 3.

The Modern FPMT technique is communicated to the board communication and control module for control purposes and then to the main controller of the cabinet so we can show the analysis and parameters of the returned back signal (reflected test signal). The multiplexer module is used to multiplex the traffic signal coming from the EDFA side with the test signal coming from the FPMT side. The EDFA board sends the test signal using different wavelengths to avoid interference with the system and traffic [16]. The wavelength of the EDFA board in DWDM is C band; it runs from 1525 nm to 1565 nm and from 191 to 196 THz [49,50].

The FPMT detection circuit should be merged with an EDFA board at the transmit and receive sides, as shown in Figure 4 and Figure 5, in order to send test signals generated by the FPMT technique within the optical DWDM network in the same pass.

5. The Maximum Performance Monitoring Distance by Using EDFA Board

Several optical networks have been monitored and tested depending on performance and design parameters at a PON-DWDM network for Huawei and Nokia. The authors developed the equation considering the sources of losses and parameters that affect an optical signal pass through the network and the max. transmit distance.

P_Tx = P_Rx + ᾳ_FL

(1)

ᾳ_FL = EFM_Max + ᾳ_B + ᾳ_Max

(2)

where P_Tx represents the value of transmit power, P_Rx represents the sensitivity of received power, ᾳ_FL represents every fiber line attenuation, ᾳ_Max represents the max. attenuation based on distance, EFM_Max represents the max. effective fade margin at the system, and ᾳ_B represents the other basic attenuation over fiber passes. The max. effective system fade margin can be around 4 dB, and the maximum other basic attenuation over the fiber line (bending, scattering, nonlinear effects, others) can be around 3 dB.

The maximum P_Tx from the EDFA board is around 26 dBm, and the maximum PRx at the EDFA board is around −48 dBm [48].

In this situation, the EDFA board results are as follows: at substitute in Equation (1), the EFM_Max at the fiber line will be around −74 dB, and in Equation (2), the ᾳ_Max will be around −67 dB.

D_Max = α_Max/ᾳ_p

(3)

where D_Max represents the maximum transmitting distance to the signals and ᾳ_p represents the attenuation coefficient of the fiber.

The test signal through fiber lines carries over SMF-G.652 [54,55]. The wavelength SMF G.652 with the best transmission performance is 1550 nm, with an attenuation value of around 0.22 dB/km [17,54,55].

To obtain the maximum performance monitoring distance equation using EDFA board integrated with the modern FPMT by applying the expression derived in Equation (4).

ᾳ_Max = (D_Max × ᾳ_p) + α_FPMT

(4)

where ᾳ_FPMT represents the modern FPMT technique insertion loss and is around 0.4 dB [16].

In this situation, the authors used the EDFA to transmit a substitute test signal in Equation (4). The result showed that the maximum support distance testing and performance monitoring between two nodes using the EDFA board was up to 300 km.

The maximum support distance testing between nodes A&B and all signals flow wavelengths over fiber passes, as shown in Figure 6.

6. Results

The experimental investigations were performed using a Huawei lab, and according to the analyzed reflected test signals, research results were collected using the Huawei Network Cloud Engine (NCE) server optical transmission tool [55,56]. The system is applied by remotely accessing Huawei-Labs infrastructure nodes.

The specification of the implemented system for monitoring and analyzing the fiber line and collecting the information are shown in

Table 4. The requirements for the implemented system were to monitor and analyze the fiber line and collect results.

Requirement	Parameter
Experimental investigations implemented	Huawei-Labs
Experimental investigations collected	Huawei NCE servers
System applied	High-speed capacity DWDM system
Standard optical fiber	SMF G.652
Wavelength	1550 nm
Fiber attenuation coefficient	0.22 dB/km
Events created on the applied system	(1) Fiber Cuts (2) Fiber Contamination (3) Fiber Burning (4) Connector insertion loss (5) Interconnected Fiber Cable (6) Fiber bending

.

Practical Results

The results of the author’s thorough experimental investigations were carried out and published in this paper. The practical results identified six different types of optical layer defects that had been intentionally introduced into the system to test the viability of test signals for fiber line fault detection. The results after applying an improved FPMT technique integrated with EDFA optical amplifier were as follows:

Event (1). A fiber cut (break) is produced on the real implemented system at a distance of 8.9 km and an improved FPMT approach integrated with the EDFA board is applied to detect defects. According to the practical data in Figure 7, the reflection peak is around 8.9 km, the reflection rate is around −15 dB, and there is a certain pattern form that represents the fiber cut (break). The parameters and practical results detected at event 1 are shown in

Table 5. The parameters and practical results detected at event 1.

Detection Signals Generated by	EDFA Optical Amplifier
Fiber standard	G.652 SMF
Wavelength	1550 nm
Defect detected	The shape of a specific pattern indicated to fiber break
FPMT fault location distance	8.9 km
Reflection value	−15 dB

.

Event (2). A fiber end face contamination is produced in the actual implemented network at a distance of 0.2 km and an improved FPMT approach integrated with the EDFA board is applied to detecting defects. According to the practical data in Figure 8, the reflection peak is around 0.201 km, the reflection rate is around −4 dB and there is a certain pattern form that represents the fiber-to-end-face-contamination. The parameters and practical results detected at event 2 are shown in

Table 6. The parameters and practical results detected at event 2.

Detection Signals Generated by	EDFA Optical Amplifier
Fiber standard is	G.652 SMF
Wavelength	1550 nm
Defect detected	The shape of a specific pattern indicated fiber-to-end-face contamination
FPMT fault location distance	0.201 km
Reflection value	−4 dB

.

Event (3). A fiber end-face burning is produced in the actual implemented network at a distance of 0.45 km and applying an improved FPMT approach integrated with the EDFA board to detect defects. According to the practical data in Figure 9, the reflection peak is around 0.45 km, the reflection rate is around −8 dB, and there is a certain pattern form that represents the fiber end-face burning. The parameters and practical results detected at event 3 are shown in

Table 7. The parameters and practical results detected at event 3.

Detection Signals Generated by	EDFA Optical Amplifier
Fiber standard	G.652 SMF
Wavelength	1550 nm
Defect detected	The shape of a specific pattern indicates fiber end-face burning
FPMT fault location distance	0.45 km
Reflection value	−8 dB

.

Event (4). A large insertion loss on the connector is produced in the implemented network at a distance of 1.2 km, and an improved FPMT approach integrated with the EDFA board is applied to detect defects. According to the practical data in Figure 10, the reflection peak is around 1.2 km, the reflection rate is around −2 dB, and there is a certain pattern form that represents the large insertion loss on the connector. The parameters and practical results detected at event 4 are shown in

Table 8. The parameters and practical results detected at event 4.

Detection Signals Generated by	EDFA Optical Amplifier
Fiber standard	G.652 SMF
Wavelength	1550 nm
Defect detected	The pattern shape refers to the large insertion loss on the connector
FPMT fault location distance	1.2 km
Reflection value	−2 dB

.

Event (5). An interconnection between fiber cables is produced in the actual implemented network at a distance of 0.25 km, and an improved FPMT approach integrated with the EDFA board is applied to detecting defects. According to the practical data in Figure 11, the reflection peak is around 0.255 km, the reflection rate is around −1.4 dB and there is a certain pattern form that represents the interconnection between fiber cables. The parameters and practical results detected at event 5 are shown in

Table 9. The parameters and practical results detected at event 5.

Detection Signals Generated by	EDFA Optical Amplifier
Fiber standard	G.652 & G.653 SMF
Wavelength	1550 nm
Defect detected	The shape of a specific pattern indicates a mismatch between fiber cables
FPMT fault location distance	0.255 km
Reflection value	−1.4 dB

.

Event (6). A fiber bending is produced in the implemented network at distances of 0.5 km, 0.6 km, 0.7 km, 1.2 km, and 4 km, respectively, and the improved FPMT approach integrated with the EDFA board is applied to detect defects. According to the practical data in Figure 12, the reflection peak is around 0.5 km, 0.6 km, 0.7 km, 1.2 km, and 4 km, respectively, the reflection rate is around −2 dB, −3 dB, −4 dB, −3 dB, and −7 dB, respectively, and there is a certain pattern form that represents the fiber bending. The parameters and practical results detected at event 6 are shown in

Table 10. The parameters and practical results detected at event 6.

Detection Signals Generated by	EDFA Optical Amplifier
Fiber standard	G.652 SMF
Wavelength	1550 nm
Defect detected	The shape of a specific pattern indicates fiber bending
FPMT fault location distance	0.5 km, 0.6 km, 0.7 km, 1.2 km, 4 km
Reflection value	−2 dB, −3 dB, −4 dB, −3 dB, −7 dB

.

7. Discussion

7.1. Failures Detection

Analyzing the pattern shape of the reflected test signal is the FPMT approach for detecting fiber defects over network fiber lines in real-time and remotely. The detailed experimental investigations conducted showed a specific failure pattern for each event. All of the reflected test signal patterns discovered during fiber line detection are shown in Figure 13.

7.2. Fault Location Measurement

Figure 14 depicts the difference between the improved FPMT fault localization measured and the real fault localization. The deviation at Event 2 is approximately 0.001 km, the variance between practical measurement as determined by improved FPMT and the actual fault position, which varies between 0.201 km and 0.2 km, respectively. The deviation at Event 5 is approximately 0.005 km, the variance between practical measurement as determined by the improved FPMT and the actual fault position, which varies between 0.255 km and 0.25 km, respectively. The variance collected by improved FPMT measurement and actual results has a low deviation at events 2 and 5 and has no deviation at other events.

As stated in the experimental investigations shown in Figure 14, the average tolerance from the actual fault location is 1 to 5 m. Figure 15 shows the deviation measurements at each event.

7.3. Measurement Reliability

Utilizing the enhanced FPMT approach, the defects have been localized with 100% accuracy for events 1, 3, 4, and 6 and with great precision ranging from 99.5% to 98% for events 2 and 5, respectively. Figure 16 shows the measurement reliability for each event, with a measurement accuracy for defect location of up to ~99.9%

7.4. Techniques Comparison

There are many different methodologies for optical line performance monitoring. One is monitoring the optical layer performance, briefly discussed in Section 1.1. The proposed improved technique in this research is one method of monitoring the fiber line, especially hard and soft failures through the optical layers. The comparison between the FPMT technique integrated with the OSC board [16] and the proposed improved technique integrated with the optical amplifier EDFA board is shown in

Table 11. Comparison between FPMT techniques integrating with OSC or EDFA board.

Parameter	FPMT Integrating OSC Board	FPMT Integrating EDFA Board
Longest distance to detecting optical line defects (km)	150 km	300 km
Measurement accuracy for defect location (%)	99.8%	99.9%
Tolerance (m)	10–20 m	1–5 m
Insertion loss (dB)	1 dB	0 dB
Number of failure types detected and identified in practical experiments	5	6
Longest distance to detecting optical line defects (km)	150 km	300 km

.

The comparison between the improved FPMT technique (IFPMT) and the most recent techniques in terms of the optical layer performance monitoring methodology and evaluations of each technique are shown in

Table 12. Comparison between Improved FPMT technique and the other online technique.

Methodology	Identification Accuracy	Results (Error)	Classify Faults Type	Faults Type	Ref.
Fault detection depends on digital signal processing (DSP) algorithms detection, such as pre-error correction (pre-FEC), bit error rate (BER), received optical power (ROP), optical spectra, and filter weights.	--	0.14–1.41%	--	(a) Soft failure without displaying any types.	(2019). [39]
Fault detection depends on the digital spectra in soft failure detection (SFD) and soft failure identification (SFI).	99.55%	0.42–1.47%	--	(a) Soft failure without displaying any types.	(2020). [40]
Fault detection and localization depend on sharing the same transmitter module and performing in sequences, depending on algorithms, and one or many fiber Bragg gratings with different reflection wavelengths are allocated to each last user for failure detection.	99.22% 0.005–0.01 dB 2.4–7.7 m	0.2–0.8%	--	(a) Fiber breaks. (b) Research mentioning the possibility of measuring other errors not displayed.	(2022). [41]
Fault detection depends on the recognition of the reflection spectrum generated by each single-fiber Bragg grating.	99.5% 0.005–0.01 dB 2.4–7.7 m	0.16–0.5%	Faults characterization high accurate	(a) Fiber breaks. (b) Research mentions the possibility of measuring other errors not displayed.	(2022). [42,43]
Fault detection depends on the pattern shape of the reflection test signal generated from the FPMT technique integrated with the OSC board.	99.8% 10–20 m	0.02%	Faults characterization high accurate	(a) Fiber break. (b) Fiber contamination. (c) Fiber end-face burning. (d) Connector loss. (e) Fiber interconnection.	(2022). [16]
Fault detection depends on the pattern shape of the reflection test signal generated from the FPMT technique integrated with the EDFA board.	99.9% 1–5 m	0.01%	Faults characterization high accurate	(a) Fiber break. (b) Fiber contamination. (c) Fiber end-face burning. (d) Connector loss. (e) Fiber contamination. (f) Fiber bending.	(2022). [proposed improved technique]

.

The practical results and experimental investigations shown in Table 12 indicate the superiority of the improved FPMT technology with better performance than other research results.

8. Conclusions

In this paper, the performance of the modern FPMT technique has been improved by integrating it with optical amplifier boards. In this regard, detailed experimental investigations were conducted, and the number of failure types detected and identified in practical experiments was six types of optical layer faults.

The practical results of the IFPMT technique integrated with the EDFA optical amplifier are as follows: the improved technique detects optical layer soft and hard failures at the real-time remote location without interrupting the traffic flow, with a measurement accuracy for defect location of up to ~99.9%, a small tolerance of up to ~1 m, the longest distance for detecting optical line defects up to ~300 km, and an enhanced power budget for the system with optimum insertion-loss of up to ~0.0 dB.

The proposed integration method of the FPMT with the EDFA board provides better results with an excellent and efficient solution of fault location measurement and detection in real-time with good financial implications of the technique compared to the FPMT technique integrated into the OSC board. The practical results indicate that the IFPMT technology performs better than others researched.