Impact of Selected Input Features for Lightpath Feasibility Validation Using Artificial Neural Networks

Engineering notes

Key topics: self-driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The new advents of 5G and Internet of Things (IoT) will impact the traffic, both in volume and dynamicity, at unprecedented rates. As a result, optical transport networks should become more responsive to the traffic changes as well as to operate more closely to optimality. Therefore, the implementation of a self-driving network is being proposed as a way to achieve these targets. One of the key challenges in this environment is the automatic provisioning of lightpaths. In order to provision a lightpath, Quality of Transmission (QoT) needs to be estimated, which involves complex and time consuming computations. This work proposes the use of artificial neural networks (ANN) to speed up lightpath feasibility validation without performing full validation per request (slow) nor keeping a full database of feasible lightpaths (memory consuming). Moreover, we evaluate the impact of input features selection and number of neurons in the obtained accuracy.

Links and sources

• Official / arXiv page

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