Abstract

Quality of Transmission (QoT) estimation, where the received signal quality is predicted before deployment, plays a significant role in efficient resource utilization, such as determining the optimal transmission configuration. Traditionally, it is implemented with analytical models, and often accompanied by large link margins to account for the low estimation accuracy. Machine learning (ML) based methods have been recently demonstrated as an alternative solution with high accuracy. However, they require a large number of training data, which is often expensive to obtain in the context of QoT estimation. In this paper, we use active learning (AL) to achieve data efficient QoT estimation. A learner actively selects the training data to be labeled by applying the strategy of uncertainty sampling which favors data with high model uncertainty. A data selection algorithm compatible with the widely studied artificial neural network (ANN)-based QoT estimator is proposed and experimentally demonstrated in a fiber-wireless integrated testbed. Monte Carlo dropout (MC dropout) is utilized to calculate model uncertainty. To achieve a mean squared error (MSE) of 0.055, the number of training data can be reduced by more than 25% compared with the conventional passive ML. The algorithm is also investigated under different sampling settings and the impact of hyperparameters is discussed.

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