5G represents a paradigm shift not only in the deployment, management, and operation of networks, but also the services that can be delivered and the experiences customers can have. For 5G networks to deliver value for the operators, through both improved efficiency and top line growth, artificial intelligence (AI) has important roles to play.
All these should start with operators having full visibility of what is going on in their systems, including data of the networks, of the services running on the networks, as well as of the customers.
Data and analysis into networks cover three dimensions: quality, value, and development.
Specifically, the data gathered by the software should report the network quality down to cell level, for example which cells are generating the highest values, and what the next step network development should focus on.
Such data can then be used in multiple ways to deliver value for the operators. Actionable insights, developed through analysing the comprehensive data, can support precise network planning and value-based network construction. This is particularly meaningful when operators roll out 5G networks, when there is little historical reference. The AI tool should be able to provide precise planning solutions based on the existing 4G networks to make full use of the existing network and environment information to achieve an exact match between 5G network planning and user requirements.
When it comes to network construction, the network expansion plan should be developed by the AI tool with intelligent network prediction, scenario-based site selection and site value sorting. The output should be an agile expansion construction solution that can deliver material improvement of efficiency over the conventional, manual planning mode and flexibly support the phased network deployment.
The AI-devised intelligence will also play a key role in improving operational efficiency, especially by accurately dispatching work orders. This is because the tools, using big data and AI network analytics, will predict faults, and dispatch on-site inspections more accurately. The machine-learning mechanism means that the field data from the inspections can be compared with the prediction, therefore continuously improving the accuracy of the tool.
Another area that AI can help improve efficiency is in testing and optimisation. Drive tests, the conventional way of testing the cell parameters, can be largely replaced by virtual drive tests, which evaluates network performance more comprehensively, and the need for drive tests is minimised.
Without manual intervention, the coverage and capacity are optimised automatically using data analytics, and the power and antenna feeder are adjusted and optimised automatically. The automation will considerably shorten the optimisation time.
Service insight refers to real-time accurate analytics and insight of both voice and data services running on the system.
Most networks have legacies from 2G to LTE, and more and more are adding 5G on top. The AI tools to generate service insights need to report instantly the status of voice services on 2G and 3G networks, as well as Voice overs LTE (VoLTE), the infrastructure of which will also support 5G voice. Meanwhile, the tools should also report the status of services running over the data networks, from entertainment to industry applications, and diagnose the root cause of any failures or sub-optimal performances. The intelligence generated will be used to improve services, and to prevent the same failures from happening again.
Another critical area to apply the intelligence is to support new services. For example, when 5G is first launched, the focus is on eMBB services, including HD video streaming or VR gaming. The AI-powered tool should provide real-time video perception analysis, early deterioration warning, problem location, as well as fast service recovery.
Service intelligence will play a more critical role when telecom operators enter other vertical industries when 5G networks enable the more advanced services. These may include autonomous manufacturing, remote healthcare, IoT like autonomous vehicles and smart city, next generation retail, energy, logistics, transport, and many more. For example, for different industrial requirements in the smart factory, different slice services are selected to realize the intelligent analysis and optimisation service. Such services will generate higher return but will also come with higher demand. In turn, if the system fails to guarantee service stability or to address faults fast enough, the cost to telecom operators will be higher.
User insight should be focused on user experience.
By analysing the user experience on existing networks including their personalised requirements, the tools should be able to generate intelligence that will not only increase loyalty of current users but also attract new users, such as building the intelligent perception system of each user and each service to accurately evaluate and guarantee user perception.
More importantly, the AI-powered tool should be able to develop user profiles around defined attributes (e.g. high value customers, or data heavy users, or users of special apps). These detailed user profiles can guide personalised smart marketing. For example, the tool should help identify potential high value customers and offer customised services.
ZTE has launched the ideal tools to address these challenges.
The AI Insight, Value Operation (AIVO) solution, based on ZTE’s VMAX big data platform, has lived up to its missions for customers. The solution has been developed using ZTE’s evaluation capabilities of over 10,000 different services, as well as the repertoire of over 9,000 user profile attributes across 16 industries. It realises the full-process support of network planning, construction, maintenance, optimization and operation.
In real life, the AIVO solution has demonstrated the efficiency improvement in system deployment, management, and operation. The one-click automatic output for network planning can improve the efficiency by at least 60% compared with the conventional planning mode. The solution can also reduce the drive test time by more than 50% and the work order dispatches by 45%, as well as improving the site visit efficiency by more than 25%.
When used for problem locating, AIVO has reached 80% accuracy rate and has reduced the location time to within 30 minutes. As for the marketing support, it realizes the personalized precision marketing implemented by building user profile system, locking target users and pushing marketing information. The benefit of marketing support is reflected in the 40-fold increase in the customers’ subscription conversion rate.
In addition to the powerful data gathering and analytics capability, AIVO solution is also able to present the data and analytics in the most intuitive visualised way, from network traffic to customer complaints and everything in between, so that immediate actions can be taken when needed.
We are, therefore, confident that the AIVO solution is an ideal answer to telecom operators’ challenges when they embark on 5G commercialisation, both for efficiency improvement and for generating new revenue and profit through strong customer engagement.
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