Smart Factory

Network testing in private networks

Campus networks - Private networks

5G brings the possibility of faster and safer operations as well as new capabilities and efficiencies in industrial processes. Still, it comes with increased complexity and performance demands for the network. Accurate and insightful testing at every phase of the mobile network rollout helps to prepare, deploy, and operate smart factories faster while using network resources more efficiently and being aware of issues before they become critical.


Smart factory - test phases

What test phases are necessary to support high reliability wireless connectivity in a smart factory?

  • Rollout preparation - spectrum clearance
  • Site acceptance testing
  • Coverage and performance testing
  • 24/7 real-time service quality monitoring
  • Regulatory compliance
Smart factory poster

Free poster: 5G brings smart factories to a new level

Understand the complexity of smart factories and how network testing solutions from Rohde & Schwarz help maximizing the quality and performance of campus or private networks.

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Webinar: Smart Factories - 5 phases of 5G network testing

Webinar: Smart Factories - 5 phases of 5G network testing

Watch this webinar to discover how accurate and insightful testing at every phase of the mobile network to prepare, deploy and operate Smart Factories enables faster roll out, efficient use of network resources and delivery of demanding Quality of Service requirements.

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How to verify 5G network deployment and operation in smart factories

Video: How to verify 5G network deployment and operation in smart factories

In order to deploy and operate a 5G network in smart factories, this video shows the different test phases, which are necessary to support high reliability wireless connectivity in a smart factory.

5G network interactivity test

Video: 5G network interactivity testing

With the interactivity test, operators can measure the fundamental 5G network performance characteristics of throughput, latency and continuity and derive network quality for real-time applications like eGaming and AR/VR in a single integrated test.

Machine Learning - Anomaly Detection

Video: Machine Learning - Anomaly Detection

What do Liverpool FC and mobile network quality analytics have in common? Find out by watching the video, and learn how the time-based anomaly detection feature in our analytics software SmartAnalytics helps you to detect and identify network issues instantaneously, providing effective guidance to network optimization.

Machine Learning - Call Stability Score

Video: Machine Learning - Call Stability Score

The patent-pending Call Stability Score technology in our analytics software SmartAnalytics uses a machine learning/binary test-scoring concept that identifies risky areas proactively. Telephony service is not the ultimate priority for Smart Factories, but the binary test scoring concept itself can be adapted to other use cases.

Webinar: 5G NR for industrial applications

This webinar illustrates the 5G capabilities to support ultra-reliable low latency services available with 3GPP Release 15 and 16. Findings are presented from both a leading cellular test solution provider perspective as well as from the view point of a potential adopter of the 5G NR technology in its own factories.

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Further Information

Network tests pave the way for smart factories

5G has everything future smart factories need for wireless connectivity, but network tests are essential.

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White paper interactivity test

White paper: interactivity test

Learn how to simultaneously measure the fundamental performance characteristics of 5G mobile networks.

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Machine learning - anomaly detection

Blog post: Machine learning: Anomaly detection

Anomaly detection is a much-appreciated tool by data scientists. It aims to find anomalous samples or outliers and it provides valuable insight into mobile network quality and performance.

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Machine Learning - call stability score

Blog post: Machine learning - Call Stability Score

In this post, we will demonstrate how we can leverage Machine Learning to extract more value out of the data. For this, we will use the practical use case of measuring the call drop ratio.

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