Forecasting Codeword Errors in Networks with Machine Learning

Abstract: With an increasing demand for rapid high-capacity internet, the telecommunication industry is constantly driven to explore and develop new technologies to ensure stable and reliable networks. To provide a competitive internet service in this growing market, proactive detection and prevention of disturbances are key elements for an operator. Therefore, analyzing network traffic for forecasting disturbances is a well-researched area. This study explores the advantages and drawbacks of implementing a long short-term memory model for forecasting codeword errors in a hybrid fiber-coaxial network. Also, the impact of using multivariate and univariate data for training the model is explored. The performance of the long short-term memory model is compared with a multilayer perceptron model. Analysis of the results shows that the long short-term model, in the vast majority of the tests, performs better than the multilayer perceptron model. This result aligns with the hypothesis, that the long short-term memory model’s ability to handle sequential data would be superior to the multilayer perceptron. However, the difference in performance between the models varies significantly based on the characteristics of the used data set. On the set with heavy fluctuations in the sequential data, the long short-term memory model performs on average 44% better. When training the models on data sets with longer sequences of similar values and with less volatile fluctuations, the results are much more alike. The long short-term model still achieves a lower error on most tests, but the difference is never larger than 7%. If a low error is the sole criterion, the long short-term model is the overall superior model. However, in a production environment, factors such as data storage capacity and model complexity should be taken into consideration. When training the models on multivariate and univariate datasets, the results are unambiguous. When training on all three features, ratios of uncorrectable and correctable codewords, and signal-to-noise ratio, the models always perform better. That is, compared to using uncorrectable codewords as the only training data. This aligns with the hypothesis, which is based on the know-how of hybrid fiber-coaxial experts, that correctable codewords and signal-to-noise ratio have an impact on the occurrence of uncorrectable codewords.