# How to Benet From Wavelength Converters In Optical Networks

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## RWA in Wavelength Continuous Network

Trac demands are typically assumed to be either static (demands are known before- hand) or dynamic (demands arrive unexpectedly with random holding times). RWA for the static assumption is also known as the network planning phase, which typi- cally occurs before a network is deployed, consists of processing a large set of demands (trac matrix) at one time. Therefore, the main emphasis of network planning is on accommodating the trac matrix and minimizing the resources needed, such as num- ber of wavelengths, bers or number of transceivers in a network; alternatively, it aims to allocate the highest number of lightpaths, e.g. minimize the blocking, for a given number of wavelengths and a trac matrix. On the other hand, for the dynamic trac assumption, demands are generally processed sequentially. It is known as the network operation phase and it aims to set up lightpaths and assign wavelengths in a manner which minimizes the blocking or maximizes the number of connections in the network at any time taking into consideration that requests need to be processed online and thus, the solution must be computationally simple. Therefore, in this section we will introduce dierent approaches that have been suggested in literature to handle the RWA problem.

### Wavelength Converters in Optical Networks

After the overview above about optical network designs fundamentals, this section deals with the design of optical network including wavelength converter. Thus, it is divided in two parts: in the rst part, we will dene the benets of wavelength conversion in a network, the types of wavelength converters and their required characteristics by operators and network designers are explained; in the second part, the conditions to benet of wavelength converters in optical networks are presented.

#### Wavelength Conversion Benets

As we mentioned in Sec. 2.1, there are two constraints to be considered when establishing a lightpath, the WCC and the clash constraints. However, these constraints results in a fragmented wavelength resource usage over the course of network operation, which leads to a low utilization network resources. The problem is illustrated by Fig. 2.2: initially, the connection from node A to node B is blocked knowing that there is a free wavelength on each of the links between the two nodes, however, they are of dierent indexes and consequently, the connection is blocked. In contrast, the advent of a wavelength converter in the intermediate node enabled a successful connection between the nodes A and B, by converting the wavelength of the lightpath. As a result, the wavelength converter enabled us of beneting from the available wavelength resources. Hence, it improved the link utilization and consequently, it reduced network blocking.

How to Benet From Wavelength Converters In Optical Net- works

After showing the advantage of wavelength converters in terms of link utilization and how can they enhance network blocking performance, we need to understand how can we benet from wavelength converters in optical network. Therefore, in this section, we will go through the parameters or the factors that impact the usefulness of wavelength converters in WDM optical networks:
1. Topological dependence:
In wavelength-continuous networks, the longer in hop-length H, the route between s and d, the harder to nd free common wavelength to satisfy the WCC, thus, the higher the network blocking will be. That’s why the network diameter D (which is dened as the maximum over all pairs of nodes of the hop-length of their dened routes) is considered an important factor in improving network blocking perfor- mance. However, in wavelength-convertible networks, the impact of increasing D is less dramatic because a connection can access any wavelength on each link along a route. Therefore, the longer the network’s diameter, the more the divergence in performance between networks with and without wavelength conversion capability, which means an increased gain of using wavelength converters [38, 39].

Acknowledgments
List of Figures
List of Tables
List of Abbreviations
1 Introduction
1.1 Optical Networks Evolution
1.1.1 Towards All-Optical Networks
1.1.2 Optical Networks Capacity Improvement
1.2 Motivation and Objective
1.3 Thesis Structure
2 Wavelength Convertible Networks Design
2.1 Introduction
2.2 RWA in Wavelength Continuous Network
2.2.1 Optimization Approaches
2.2.2 Decoupling RWA
2.2.3 PLI-RWA
2.3 Wavelength Converters in Optical Networks
2.3.1 Wavelength Conversion Benets
2.3.2 How to Benet From Wavelength Converters In Optical Networks
2.4 Conclusion
3 Transmission Layer Model
3.1 Introduction
3.2 Transmission Impairments
3.2.1 Linear impairments
3.2.2 Non-linear Impairments
3.3 Compensated Transmission
3.3.1 MLR Model
3.3.2 System Performance
3.4 Uncompensated Transmission Model
3.4.1 GN-model
3.4.2 IGN-model
3.4.3 System Performance
3.5 Conclusion
4 Simulation Results: Oine Trac assumption
4.1 Introduction
4.2 Network Model
4.2.1 Simulation Parameters
4.2.2 Performance Parameters
4.3 Simulation Results
4.3.1 Sensitivity Of The Gain Of Using Wavelength Converters to TSO
4.3.2 TD vs DI in Wavelength Continuous and Wavelength Convertible Networks
4.4 Conclusion
5 Simulation Results: Online Trac assumption
5.1 Introduction
5.2 Routing, Modulation Format and Wavelength Allocation Algorithms
5.2.1 Heuristic algorithm
5.2.2 Heuristics Flavors
5.2.3 Simulation Parameters
5.2.4 Performance Metrics
5.3 Simulation Results: The Gain of Using OEO-WCs
5.3.1 Sensitivity to the Number of Alternative Paths
5.3.2 Sensitivity to Wavelength Assignment Algorithms
5.3.3 Sensitivity to Routing algorithms
5.4 Simulation Results: Network Gain from Using AO-WCs
5.5 Conclusion
6 Conclusions and future work
6.1 Summary and Conclusions
6.2 Future Research Proposals
Bibliography

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