International Telecommunication Union (ITU) approves that machine learning-based algorithms such as sQLEAR for 5G voice quality testing including Voice over LTE (VoLTE), Voice over New Radio (VoNR), and OTT voice. The sQLEAR algorithm (speech Quality by machine LEARning) is the world’s first ML-based standard for IP-based mobile voice quality testing and ML-based standard for voice quality modeling, also applicable to QoE testing of mobile all-IP voice services. So, now let see how to enhance 5G voice quality testing with an ML-based approach along with Smart 5g tester, 5G test equipment, 5g network tester tools and Smart 4G Testing RF drive test software, Cellular LTE RF drive test tools & equipment in detail.
The sQLEAR algorithm takes network parameters and standardized voice codec, exploits ML capabilities to describe the impact on voice quality of voice codec, the increasingly complex network that is inherent in the all-IP voice networks (VoLTE, VoNR), and client information – empowers operators with cost-effective, network-centric monitoring, optimization, troubleshooting and benchmarking of their 4G and/or 5G networks, without the need to individually test all commercial devices.
sQLER algorithm is a machine learning that provides mobile operators with the network-centric, device-agnostic, audio path-independent and provides a real-time view of the true voice quality through their 4G and 5G networks, which reduces both cost and time to market of new 5G voice services, while cost-efficiently maintaining high-quality standards for existing VoLTE services. This enables operators not only to save time and money by optimizing their networks for all but also quickly identify any network-based issues without interference from device characteristics.
Fifth-generation (5G) technology potentially merges multiple networks on a single platform, providing a landscape for seamless connectivity, particularly for high-mobility devices and will play a vital role in future wireless networks and the innovation of 5G technology will set free a massive Internet of Everything (IoE), where billions of connected devices, people, and processes will be served. The 5G services include several use cases enabled by the enhanced mobile broadband, massive machine-type communications, ultra-reliable low-latency communication, and Fifth-generation networks or 5G networks are prone to various research challenges. These features help us to provide a comprehensive survey on 5G technologies that emphasize machine learning-based solutions to cope with existing and future challenges.
Now a question arises, what are the challenges of evaluating 5G voice quality?
Voice quality testing technology is the global standard for operators benchmarking and the quality of their 2G, 3G, and circuit-switch 4G voice services. Some of the challenges of assessing voice QoE in IP-based 5G networks are –
- Perceptual audio quality assessment algorithms (Highly sensitive and manually tuned) make the accurate evaluation of all IP voice quality will get difficult. (Accuracy)
- Significant differences between device models can result in a single faulty device corrupting test results and misleading QoE scoring. (Device dependence)
- Whether it’s endless introduction of new proprietary codecs, or client versions and encryption algorithms in OTT services along with time and cost to manual tuning of test protocols. (Evolution of technology)
- Increased interdependency complexity between network KPIs in an increasing number makes voice quality measurement more difficult. (Complication)
Use cases for sQLEAR in terms of enhancing 5G voice quality testing:
- Network monitoring of service quality of all IP voice services with ML-based intrusive parametric QoE prediction algorithm.
- Network optimization and troubleshooting of voice service quality issues on an IP/RTP bitstream level effectively.
- Network benchmarking for IMS and OTT voice quality with consistency, backward compatibility, and equivalent scoring across devices.
- Identify suitable devices or device confirmation to accurately assess the voice quality of VoNR, VoLTE, and OTT services.
Conclusion
A network-centric approach is built on machine learning to enhance your voice quality testing (5G). But it will be critical to your ability to deliver voice services that can compare on quality versus OTT rivals, here comes network testing or measurement through the RantCell tool. RantCell is a SaaS-based software that you can install on your Android phone to monitor the network on 5G/4G coverages. Network testing for indoor testing, drive testing, crowdsourcing, and many more through RantCell will give you with the best solution.