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By | March 17, 2020 | 355 views

Machine learning: Time-based anomaly detection (part 3)

Anomaly detection is a much-appreciated tool by data scientists. It aims to find data samples that do not conform to the regular distribution of the dataset to which they belong. Finding anomalous samples, also known as distribution outliers, provides valuable insight that often correlates with defects or errors in the data collection process (e.g., faulty or misconfigured equipment). This blog…

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By | May 2, 2019 | 2,885 views

Machine learning use case: Call Stability Score (part 2)

In our previous post about machine learning (ML), we introduced the topic of artificial intelligence (AI) and hinted at the typical deep learning pipeline that is needed to apply ML on data collected with mobile network testing solutions from Rohde & Schwarz. In this post, we will demonstrate how we can leverage ML to extract more value out of the…

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By | November 7, 2018 | 2,079 views

Machine learning in mobile network testing (part 1)

Artificial intelligence (AI) has vast applications in the telecom industry, from anomaly detection to network optimization and utilization; and AI could lead to various new revenue streams. Many telecom leaders believe that machine learning (ML) will be the key to derive and infer user behavior and expectations. It aims to increase the user’s quality of experience (QoE) and reduce customer…

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