Novel evolutionary planning technique for flexible-grid transmission in optical networks
Introduction
The dynamism of today's data traffic demands has motivated the development of more flexible and faster telecommunication systems to cope with the heterogeneity of services and escalating requirements of user's consumption habits. In these systems, the necessity of physical-layer-aware methods to improve quality of transmission (QoT) becomes critical in order to integrate the underlying transmission aspects into the network planning [1,2] and provide more reliable resource allocation. In this regard, the emergence of channel models [3] that can accurately estimate physical layer impairments, especially nonlinear interference (NLI), has offered a framework where provisioning strategies can rapidly assess the QoT and provide more efficient decisions.
NLI in optical transmission is a theme that has been around in the field of optical communications since the early 90s [4]. Nevertheless, it was a while back when a reasonably accurate and low complexity model, named Gaussian noise (GN) [3], was proposed and later applied on several works [[5], [6], [7]] that combine it to diverse transmission scenarios. In a general sense, this model proposes treating the noise generated during the propagation of light as the sum of a contribution derived from the linear regime, originated in optical amplifiers (e.g., Erbium doped fiber amplifier (EDFA)), and a portion that arises from the nonlinear physical interaction between fiber and light. The latter is subdivided into two types of effects: self-channel interference (SCI), which comprises the noise that the optical channel triggers on itself, and cross-channel interference (XCI), that considers the mutual interaction of neighboring channels. To deal with these impairments, the design of physical-layer-aware resource allocation techniques has become of key importance in the scope of Optical Networks.
A brief survey in the literature shows that the term “Resource Allocation” is predominantly observed in the process of assigning network assets (e.g., spectrum, route) to support strategic goals, i.e., maximize the signal-to-noise ratio (SNR) margin, minimize the total allocated bandwidth, etc. Numerous techniques have been extensively employed in optical communications to optimize resource allocation in network traffic, such as linear/nonlinear programming [6,7] and metaheuristics. The latter is based on stochastic optimization inspired from miscellaneous fields (e.g., Game Theory [8], Swarm Intelligence [9,10], Evolutionary Algorithms [11]) and has given evidence of aptness in providing a derivative-free method that yields sufficiently good results with incomplete datasets or limited computation capacity.
The method derived in this manuscript proposes the usage of an evolutionary metaheuristic – the non-dominated sorting genetic algorithm II (NSGA-II [12]) – to optimize resource allocation for offline planning when applied to point-to-point and meshed networks. This paper compares its numerical results with the benchmark provided by Ref. [6], given that analogous scenarios are assumed. Three Pareto analyses for the link-level approach are described. The first analysis focuses on the conflicting relationship between the minimization of total bandwidth vs. maximization of the minimum SNR margin. The second maintains the minimization of the total bandwidth, but it maximizes the sum of SNR margins. The third investigation is proposed to minimize the sum of SNR margins while maximizing the minimum SNR margin. The latter is a reformulation of what is proposed in Ref. [6], in which one of the main goals is to maximize the SNR margins of all channels, condition that may lead to over-provisioning of resources. As a result of this reformulation, spectrum resource savings can be obtained for specific transmission intervals. At last, the evolutionary algorithm provides a more substantial whole-network planning strategy, where the provisioning is adapted to optimize the blocking and total spectral usage for various meshed network topologies.
The remainder of the paper is structured as followed. Sec. 2 presents the optimization problem and the elements of nonlinear channel model theory needed for this work. In Sec. 3, the results for the link-level optimization are introduced, while Sec. 4 extends the algorithm to be applied in meshed networks. Finally, Sec. 5 draws the conclusions.
Section snippets
Problem statement
In the following analysis, we considered the same network topology of [6], which is portrayed by Fig. 1. In this point-to-point, there are three types of channels. Channels of type A (in blue) propagate from nodes 1 to 3. Channels of type B (in orange) are available for only half of the total network length, i.e., from node 1 to 2. At last, channels of type B+ (the symmetric representation of B, also in orange) go from node 2 to 3. Channel A demands a bit rate of 200 Gbps, whereas B and B+ of
Hyperparameter optimization
The selection of some of the algorithm's most important parameters (σp, Nindividuals, PrM) is a Hyperparameter Tuning (HT) problem [18]. To solve it, we carried out a grid-search assuming: Nindividuals ∈ {25, 50, 100}, PrM ∈ {2.5%, 10%, 40%} and σp ∈ { − 20, −10, 0, 10} dBm to minimize the required number of iterations in a pre-computed channel configuration scenario where the solution is known a priori. After this optimization was executed, the set [Nindividuals = 50, PrM = 10%, σp = −10 dBm]
Meshed network approach
In this section, the studied technique is adapted to be used in meshed network applications. First, a traditional RSA (routing and spectrum assignment) strategy was upgraded to consider the physical layer. After that, the NSGA-II was implemented to optimize the best set of modulation formats and launch power for all requests in the network, aiming to minimize the number of blocked channels and bandwidth usage. The proposed technique was assessed in ring networks and 15 different meshed
Conclusion
In this paper, we presented a novel resource allocation technique as a means to optimize the usage of spectrum resources and power allocation in flexible-grid link-level and meshed networks under nonlinear physical impairment regime. The similarity between optical spectrum and genetic features helps the implementation of an evolutionary-based optimization technique that permits a trade-off analysis in multi-objective scenarios. Due to the demanding network requirements to reduce SNR-margin, a
Author statement
Matheus Sena: Conceptualization, Methodology, Writing- Original draft preparation, Software, Data curation. Pedro Freire: Conceptualization, Methodology, Writing- Original draft preparation, Software. Leonardo Coelho: Writing- Reviewing and Editing. Alex Santos: Data curation. Antonio Napoli: Writing- Reviewing and Editing. Raul Almeida: Conceptualization, Methodology, Writing- Original draft preparation, Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work has received partial funding from the EU Horizon 2020 program under the Marie Skodowska-Curie grant agreement No. 813144 (REAL-NET) and the Brazilian National Council for Scientific and Technological Development (CNPq), as well as the institutional support from UFPE and UFBA.
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