 Open Access
 Total Downloads : 13
 Authors : Dil Afroza Jerin , Md. Omar Faruk
 Paper ID : IJERTV8IS090254
 Volume & Issue : Volume 08, Issue 09 (September 2019)
 Published (First Online): 07102019
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Identification of Optimal Light Path in a WDM Network
Dil Afroza Jerin
Department of Information and Communication Engineering
Pabna University of Science and Technology Pabna, Bangladesh
Md. Omar Faruk
Department of Information and Communication Engineering
Pabna University of Science and Technology Pabna, Bangladesh
Abstract A WDM network based on dynamic lightpath allocation, we have to take into consideration the physical topology of the WDM network and the traffic requirements. The physical topology is defined by the nodes, typically computers that generate data to be transmitted where data is needed, the optical routers that determine how the optical signals are sent towards their respective destinations, and the fiber connections that provide the physical medium for communication. When we want to design an optical network, we think about optimal or best path for installing the network. According to the result of this research, the power budget and quality factor have been calculated from the given networks. The result shows that the path is the best one due to less power budget and high quality factor value and we shall also show the characteristics of the power budget and quality factor from the graph which is simulated in MATLAB.
Keywords WDM network, Quality factor, Power budget, Receiver sensitivity, Transmit power

INTRODUCTION
An optical fiber is a flexible, transparent strand of very pure glass that acts as a light pipe to transmit light between two ends of the fiber. Optical fibers have a core surrounded by a cladding layer made of dielectric material. The optical signals in the core are confined by establishing a refractive index that is greater than the cladding. Optical fibers are used as a medium for telecommunication and networking. Light in a fiber optic cable travels through a core by constantly bouncing from the cladding, a principle termed total internal reflection. As the cladding does not absorb any light from the core, light waves travel longer distances. [1] Power budget refers to the amount of loss a data link can tolerate while maintaining proper operation. In power budget calculation different types of losses are also be calculated such as connector loss, splice loss, fiber loss. Attenuation causes reduction in signal strength or light power over the distance (length) of the lightcarrying medium. Fiber attenuation is measured in decibels per kilometer (dB/km). Connector and splice loss in optical fiber is caused by different factors, which includes lateral and axial misalignment that occurs when the axes of the two fibers are offset in a perpendicular direction and angular misalignment which occurs when the axes of two connected fibers are no longer parallel. Coupling losses due to fiber alignment depend on fiber type, core diameter, and the distribution of optical power among propagating modes. Optical fiber offers better performance than other transmission media because it provides huge
bandwidth with low attenuation. This allows signals to be transmitted over longer distances by using less regenerators or amplifiers, which reduces the cost and improves signal reliability. The recent developments in optical devices and network technologies use multiple optical signals on the same fiber is called wavelength division multiplexing (WDM). In WDM network design we face a problem to determine the optimal lightpath from a set of all possible paths in a network topology. Based on the power budget calculations and quality factor calculations we can easily find the best light path. In this thesis, we do the same thing and simulated this result into MATLAB and get the different types of graphs of power budget and quality factor. We also find out the relation between power budget and quality factor. A connectivity matrix is always a square matrix. a number of rows and cells equivalent to the number of nodes in the network. Each cell representing a connection between two nodes receives a value of 1. Each cell that does not represent a direct connection gets a value of 0.[5]

METHODOLOGY
Methodology is the systematic, theoretical analysis of the methods applied to a field of study. It comprises the theoretical analysis of the body of methods and principles associated with a branch of knowledge.

Power Budget
Power budget refers to the amount of loss a data link can tolerate while maintaining proper operation. In other words, it defines the amount of optical power available for successful transmitting signal over a distance of optical fiber. Power budget is the difference between the minimum (worst case) transmitter output power and the maximum (worst case) receiver input required. The calculations should always assume the worstcase values, in order to ensure the availability of adequate power for the link, which means the actual value will always be higher than this.[2] Optical power budget is measured by dB, which can be calculated by subtracting the minimum receiver sensitivity from the minimum transmit power:
PB(dB) = PTX (dBm) PRX (dBm) (1)
Power Budget Equation for Single link, using power budget equation (1), we can write
(2)
Where, (3) [3]
A fiber link pair (i, j) where, i and j represents source and destination nodes respectively. Pt is the power launch from source node i into the fiber. Rs is receiver sensitivity. Optical communication system uses a bit error rate (BER) value to specify performance requirements. To achieve a desired BER a minimum average optical power value must arrive at the receiver end. This value is called receiver sensitivity.
f is the fiber attenuation constant , L is length of fiber in between source and destination.
Lc is link loss, c is connectors loss, s is splices loss and N1 is the number of connectors and N2 is the number of splices used.
Sm is the system margin taken so that it will incorporate for component aging, temperature fluctuations and losses arising from components that might be added in future. Power Budget Equation for Multiple Links, The overall power budget (Pboverall) is given by
(4)
Where, p all possible lightpath and (k, l) are node pair.[4]

Quality Factor
QFactor of a lightpath is defined as the ratio of output power relative to input power. It is normalized by dividing the value of QFactor with maximum value of Qfactor possible. It is expressed in percentage. So 100% QFactor means lightpath has the highest QFactor and the lightpath corresponding to this value of QFactor will be the best light path. To maximize the QFactor we need to maximize the output power for constant value of input power. To maximize the QFactor we need to maximize the output power for constant value of input power. We know that output power received is the attenuated version of input power due to attenuation loss, splice loss and connector loss. So we should try to minimize the losses in the optical fiber communication. Losses can be reduced by selecting the best components like connectors, splices and optical fiber which are having minimum power loss values. Out of all possible lightpaths, the lightpath having minimum power loss should be selected as optimal lightpath. QFactor is defined for a lightpath as the ratio between output power and input power.[4]
If Pin is the input power, Pout is the output power and Pb is the overall power budget of a lightpath having multiple links, then we can propose to define QFactor (QF) as

NETWORK DESIGN AND CALCULATION
1
2
0 1 1 0 1
1 0 0 1 1
1 0 0 1 1
0 1 1 1 0
5
3 4
Figure1.1: Network Topology 1
Figure1.2: Connectivity Matrix
Table: 1.1: All possible paths power budget and quality factor values are given in a table.
All Possible Length ofPath (km)
Paths
Power Budget(db)
Quality Factor (100%)
1. 124
30
50.5
152
2. 134
130
.5
97.5
3. 154
90
20.8
4
4. 1354
125
2.70
86.5
5. 13524
130
0.1
*
6. 1524
95
17.1
14.5
1
2
0
1
1
0
1
0
0
1
0
0
5
3 4
1
0
0
1
1
0
1
1
0
1
0
0
1
1
0
Figure2.1: Network Topology 2 Source1 Destination 5
Figure2.2: Connectivity Matrix
Table: 2.1: All possible paths power budget and quality factor values are given in a table
All Possible Paths
Length of paths(km)
Power Budget (db)
Quality Factor (100%)
1. 135
78
6.5
50
2. 1345
83
3.4
74
3. 1245
87
11.4
12.3
4. 12435
122
16.7
*
All Possible Paths
Length of paths(km)
Power Budget (db)
Quality Factor (100%)
1. 135
78
6.5
50
2. 1345
83
3.4
74
3. 1245
87
11.4
12.3
4. 12435
122
16.7
*
(5)
0
1
1
1
0
0
2
1
0
0
1
1
1
1
6
1
0
0
1
0
0
1
1
1
0
1
0
0
1
0
1
0
1
0
1
0
0
1
0
5
3
4
Figure3.1: Network Topology 3 Source 1 Destination 6
Figure3.2: Connectivity Matrix
0
1
1
1
0
0
2
1
0
0
1
1
1
1
6
1
0
0
1
0
0
1
1
1
0
1
0
0
1
0
1
0
1
0
1
0
0
1
0
5
3
4
Figure3.1: Network Topology 3 Source 1 Destination 6
Figure3.2: Connectivity Matrix
0 1 0 0 0 0 1 0
2 4 1 0 1 0 0 0 0 0
0 1 0 1 0 0 10
0 0 1 0 1 1 0 0
3 6 0 0 0 1 0 1 0 0
5 0 0 0 1 1 00 1
1 0 1 0 0 0 0 1
1 0 0 0 0 0 1 1 0
7 8
Figure5.1: Network Topology 5 Source 1 Destination 8
Figure5.2: Connectivity Matrix
Table: 3.1: All possible paths power budget and quality factor values are given in a table.
All Possible Paths
Length of Paths (km)
Power Budget (db)
Quality Factor (100%)
126
1.3
57.98
44
1256
70.8
29.58
26
12456
66.4
30.74
23
13426
205.5
24.9
*
1456
72.6
28.26
29
1426
37.5
42.9
7
14256
107
14.5
63
Table: 5.1: All possible paths power budget and quality factor values are given in a table.
All Possible Paths
Length of Paths (km)
Power Budget (db)
Quality Factor (100%)
1. 178
60
23.5
30
2. 12378
69
18.7
3.9
3. 123468
70
17.7
1.7
4. 12345
68
94
7.5
58
5. 173468
79
15.3
15
6. 17345
68
103
3.9
78

SIMULATION RESULT
We have considered network topology as shown in Figure 1.1 having 5 nodes. There our source is 1 and destination is 4.The links are shown by the line joining between two nodes. The network topology considered has the connectivity matrix as shown in Figure 1.2. We calculated power budget and quality factor for network topology which is shown in table
1.1. Following the same process, we calculate power budget and quality factor and draw a graph usingfollowing data in MATLAB. The simulation result is given below:
We get the following graph for those values of power budget which is given in table 1.1. The graph is given below:
Graph of Power Budget
1 2 7 6 3 4 5 
0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 1 0 
Figure4.1: Network Topology 4 Source 1 destination 7 
Figur4.2: Connectivity Matrix 
1 2 7 6 3 4 5 
0 1 1 0 0 0 0 1 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 0 1 0 1 0 0 1 0 0 0 0 1 1 0 
Figure4.1: Network Topology 4 Source 1 destination 7 
Figur4.2: Connectivity Matrix 
60
50
Power Budget(db)
Power Budget(db)
40
30
Table: 4.1: All possible paths power budget and quality 20
All Possible Paths 
Length of Paths (km) 
Power Budget (db)_ 
Quality Factor (100%) 
1. 1267 
49 
8.3 
44 
2. 13467 
41 
12.4 
17 
3. 13457 
38 
12.7 
15 
4. 126457 
58 
4.1 
73 
All Possible Paths 
Length of Paths (km) 
Power Budget (db)_ 
Quality Factor (100%) 
1. 1267 
49 
8.3 
44 
2. 13467 
41 
12.4 
17 
3. 13457 
38 
12.7 
15 
4. 126457 
58 
4.1 
73 
factor values are given in a table. 10
0
10
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Number of Path
Figure 6.1: Graph of Power Budget and Reference Path Number
Graph of Quality Factor
100
Graph of Quality Factor
80
70
80
Quality Factor
Quality Factor
60
Quality Factor
Quality Factor
50
60
40
40 30
20
20
10
0
20
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Number of Path
0
1 1.5 2 2.5 3 3.5 4
Number of Path
Figure 7.2: Graph of Quality Factor and Reference Path Number
We have considered network topology as shown in Figure 3.1
Figure 6.2: Graph of Quality Factor and Reference Path Number
We have also considered a network topology as shown in Figure 2.1 having 5 nodes. Here our source is 1 and destination is 5. The links are shown by the line joining between two nodes. The network topology considered has the connectivity matrix as shown in Figure 2.2. We calculated power budget and quality factor for network topology which is shown in table 2.1. Following the same process, we calculate power budget and quality factor and draw a graph using following data in MATLAB. The simulation result is given below:
We get the following graph for those values of power budget which is given in table 2.1. The graph is given below:
Graph of Power Budget
having 6 nodes. There our source is 1 and destination is 6.The links are shown by the line joining between two nodes. The network topology considered has the connectivity matrix as shown in Figure 3.2. We calculated power budget and quality factor for network topology which is shown in table
3.1. Following the same process, we calculate power budget and quality factor and draw a graph using following data in MATLAB. The simulation result is given below:
We get the following graph for those values of power budget which is given in table 3.1. The graph is given below:
Graph of PowerBudget
60
50
Power Budget(db)
Power Budget(db)
40
15
30
10
Power Budget(db)
Power Budget(db)
20
5
10
0
0
5 10
10
15
20
1 1.5 2 2.5 3 3.5 4
Number of Path
20
30
1 2 3 4 5 6 7
Number of Path
Figure 8.2: Graph of Power Budget and Reference Path Number
Figure 7.1: Graph of Power Budget and Reference Path Number
Graph of Quality Factor
80
Graph of Quality Factor
80
70
Quality Factor(100%)
Quality Factor(100%)
60
Quality Factor(100%)
Quality Factor(100%)
60
40
50
20 40
0 30
20
40
20
10
1 1.5 2 2.5 3 3.5 4
Number of Path
60
1 2 3 4 5 6 7
Number of Path
Figure 9.2: Graph of Quality Factor and Reference Path Number
We have considered network topology as shown in Figure 5.1
Figure 8.2: Graph of Quality Factor and Reference Path Number
We have considered network topology as shown in Figure 4.1 having 7 nodes. There our source is 1 and destination is 7.The links are shown by the line joining between two nodes. The network topology considered has the connectivity matrix as shown in Figure 4.2. We calculated power budget and quality factor for network topology which is shown in table
4.1. Following the same process, we calculate power budget and quality factor and draw a graph using following data in MATLAB. The simulation result is given below:
We get the following graph for those values of power budget which is given in table 4.1. The graph is given below:
having 8 nodes. There our source is 1 and destination is 8.The links are shown by the line joining between two nodes. The network topology considered has the connectivity matrix as shown in Figure 5.2. We calculated power budget and quality factor for network topology which is shown in table
5.1. Following the same process, we calculate power budget and quality factor and draw a graph using following data in MATLAB. The simulation result is given below:
We get the following graph for those values of power budget which is given in table 5.1. The graph is given below:
Graph of Power Budget
25
Graph of Power Budget
13 20
Power Budget(db)
Power Budget(db)
12
Power Budget(db)
Power Budget(db)
11 15
10
9
10
8
7
6 5
5
4
1 1.5 2 2.5 3 3.5 4
Number of Path
Figure 9.1: Graph of Power Budget and Reference Path Number
0
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Number of Path
Figure 10.1: Graph of Power Budget and Reference Path Number
Graph of Quality Factor
80
Graph of Power Budget vs Quality Factor
80
Quality Factor(100%)
Quality Factor(100%)
Quality Factor(100%)
Quality Factor(100%)
60 70
60
40
50
20
40
0
30
20 20
40
10
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
Number of Path
0
Figure 10.2: Graph of Quality Factor and Reference Path Number
Now we shall show the graph of power budget and quality factor and analyze that how they are related. The graph is given below:
We have got the first graph from the value of table 1.1.
Graph of Power Budget vs Quality Factor
100
Quality Factor(100%)
Quality Factor(100%)
50
0
50
100
150
200
10 0 10 20 30 40 50 60
Power Budget)
Figure 11.1: Graph of Power Budget and Quality Factor
We have got the second graph from the value of table 2.1.
20 15 10 5 0 5 10 15
Power Budget)
Fgure 11.2: Graph of Power Budget and Quality Factor
We have got the third graph from the value of table 3.1.
Graph of Power Budget vs Quality Factor
80
Quality Factor(100%)
Quality Factor(100%)
60
40
20
0
20
40
60
30 20 10 0 10 20 30 40 50 60
Power Budget)
Figure 11.3: Graph of Power Budget and Quality Factor
We have got the fourth graph from the value of table 4.1
Graph of Power Budget vs Quality Factor
80
Quality Factor(100%)
Quality Factor(100%)
70
60
50
40
30
20
10
4 5 6 7 8 9 10 11 12 13
Power Budget)
Figure 11.4: Graph of Power Budget and Quality Factor
We have got the fifth graph from the value of table 5.1.
Graph of Power Budget vs Quality Factor
80
Quality Factor(100%)
Quality Factor(100%)
60
40
20
0
20
ACKNOWLEDGMENT
I would like to express my deepest gratitude to my thesis supervisor Dr. Md. Omar Faruk for his guidance showing me the path of conducting successful research and above all for always being there as my mentor. He shared his wisdom with me in analyzing subject matters and at the same time valued my thinking approach to synthesize those topics. His suggestions drove me towards better ways of thinking, his reviews enriched me in solving problems, and his support gave me strength at the time of my disappointment. I shall forever cherish the memories of working with him. I deeply thank my friends and families for always believing in me even at the moment when I was losing my confidence.
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http//www.fiberoptictransceivermodule.com/fiberlinkpower budgetcalculation.html

https://www.fiberoptics4sale.com/blogs/archiveposts/95049798 calculatingfiberlossanddistanceestimates

https://ethesis.nitrkl.ac.in/2572/1/final_thesis.pdf


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40
0 5 10 15 20 25
Power Budget)
Figure 11.5: Graph of Power Budget and Quality Factor