Faced with competition from OTT telephony, the economics of mobile voice services is fundamentally changing. A study by Juniper Research forecast that the total number of Voice-over-5G users will reach 2.5 billion globally by 2026; rising from only 290 million in 2022. However, despite this growth, they also forecast operator-billed voice revenue will decline by 16% over the next four years, as P2P voice traffic migrates to OTT telephony apps.
It's an almost perfect storm – a rapid growth in subscribers relying on operators’ 5G voice services, coupled with steadily decreasing revenue from their voice services. And the reason? Thanks in part to the success and experience of OTT voice apps such as WhatsApp, subscribers expect hi-fidelity voice with every call they make. Meanwhile, legacy manual testing technologies leave operators struggling to optimize the quality of their own voice services.
All this makes launching new voice services over 5G New Radio (VoNR), while maintaining the voice service quality and growing voice revenue through VoLTE expansion with minimized CAPEX/OPEX, one of today’s key concerns for mobile network operators.
What are the challenges of assessing 5G voice quality?
Voice quality testing technology is over 20 years old, with POLQA, the global standard for operators benchmarking the quality of their 2G, 3G and circuit-switch 4G voice services. However, hi-fidelity IP voice brings with it fresh challenges which cannot be satisfactorily addressed by highly-sensitive, manually-tuned and device-dependent legacy approaches.
The challenges of assessing voice QoE in IP-based 4G and 5G networks include:
- Accuracy – Highly sensitive and manually tuned perceptual audio quality assessment algorithms make the accurate evaluation of modern all IP voice quality difficult
- Device dependency – Significant differences between device models can result in misleading QoE scoring, with a single faulty device corrupting test results
- Technology evolution – Continuous introduction of new proprietary codecs, client versions and encryption algorithms in OTT services add time and cost to manual tuning of test protocols
- Complexity – Increased interdependency complexity between network KPIs makes voice quality assessment complex
The need for a new approach to 5G voice quality testing
To meet the needs of today’s evolving mobile networks, there is a growing need for flexible, real-time, automated QoE-centric service evaluation, troubleshooting, and optimization. This has been driven by a number of factors. First, the volume of 4G subscribers has grown dramatically. Second, the range of 4G services has also increased. Finally, 5G network rollout is underway, bringing significantly increased network complexity, an even greater number and a larger variety of devices as well as more service diversity. As more networks, devices and subscribers turn to IP-based voice services – VoLTE, VoNR and OTT – so a new automated approach to voice quality testing is needed. To test IP voice quality effectively and efficiently, it must be device independent, network-centric and tuned by Machine Learning.
What is sQLEAR?
The sQLEAR algorithm is the world’s first ML-based standard for IP-based mobile voice quality testing approved by ITU. The sQLEAR algorithm takes network parameters and standardized voice codec and client information and uses machine learning to provide mobile operators with the network-centric, device-agnostic, audio path-independent, real-time view of the true voice quality being delivered through their 4G and 5G networks. This significantly reduces both cost and time to market of new 5G voice services, while cost-efficiently maintaining high-quality standards for existing VoLTE services.
sQLEAR’s ML-based approach enables you to evaluate QoE of all mobile IP voice services with a single comprehensive algorithm. By focusing on the network performance, sQLEAR isolates device anomalies to accurately measure subscriber voice QoE – giving you a real-time view of true voice quality and removing no need to test every individual device. Certified as ITU-T P.565.1, sQLEAR is Infovista intellectual property and just one pillar in a broader Infovista strategy of developing ML/AI-based network-centric quality of experience testing, covering OTT voice apps, eGaming and other experience-rich IP-based 5G services.
sQLEAR use cases for enhancing 5G voice quality testing
Tuned with machine learning, sQLEAR removes the device variability from the equation. This enables you to take a network-level view of your voice quality – and deliver the next generation, hi-fidelity experience that your subscribers demand and expect.
Use cases for sQLEAR include:
- Network monitoring – Monitor service quality of all IP voice services with ML-based intrusive parametric QoE prediction algorithm
- Network optimization – Optimize and troubleshoot voice service quality issues effectively on an IP/RTP bitstream level
- Network benchmarking – Compare IMS and OTT voice quality with consistency and backwards compatibility for benchmarking and comparable scoring across devices
- Device validation – Identify suitable devices to accurately assess the voice quality of VoNR, VoLTE and OTT services
Future-proof your voice quality testing in the era of 5G
The sunsetting of 2G and 3G networks is expected to peak in 2025, with the GSA reporting that at least 42 European operators are planning to switch off legacy networks and migrate full-scale to 5G. Meanwhile, over 1500 5G devices have so far been launched, including nearly 1000 different models of 5G phones. VoLTE – and ultimately VoNR – is both the present and the future of operators’ mobile voice services.
To future-proof your voice quality testing, a network-centric, device-independent approach that is built on machine learning will be critical to your ability to deliver voice services that can compete on quality versus OTT rivals.
Learn more about 4G and 5G voice quality testing
To find out more about VoLTE and VoNR user experience testing with sQLEAR, please contact us, or dig into some of the helpful resources below.
More helpful pages:
- Whitepaper: A new approach for testing voice quality
- Webinar: A Machine Learning Approach for Network Centric Voice Quality Testing
- Whitepaper: OTT telephony application testing
- Ebook: Evolving network testing – 12 use cases