Economic Modeling and Fast Planning Algorithm of Undersea Optical Cable System

【Objective】Undersea optical cable plays a leading role in international communication. In the planning of undersea optical cable communication system, several groups of possible schemes are often determined according to experience. The scheme with relatively better transmission performance and investment cost is then selected. However, with the development of communication equipment and communication technology, the number of optional modulation formats and optical fibers keeps are increasing. The number of alternative schemes is too large to quickly determine the optimal scheme. Therefore, it is necessary to establish a cost optimization model of undersea optical cable communication system to solve the cost optimal scheme.【Methods】Aiming at the problem of system planning and configuration in the undersea optical cable communication system, a cost optimization model considering the parameters of transmission performance, transmission capacity and supply voltage is established by taking the span length, optical fiber type, number of optical fiber pairs, repeater output power, modulation format and number of practical channels as the variables. The cascaded-lexicographic method is used to solve the cost optimization model.【Results】The results show that the difference between the planning results of the proposed optimization algorithm and full traversal algorithm are no more than 0.34%. However, the number of operations of the optimization algorithm proposed in this paper is far less than that of the full traversal algorithm, with only 1/100 of that of the full traversal algorithm. Secondly, the number of operations of the cascade genelexicography method is less affected by the range of variables than that of the total traversal method. Finally, by comparing the optimal cost of the undersea cable communication system under different span length, it is found that the planning results have no regularity.【Conclusion】The variables always influence each other. The optimal cost design of the system cannot be obtained according to experience, so it needs to establish a model. The algorithm used in this paper is not only accurate, but also uses fewer operation times.