INTELLIGENT BRANDS // Software for Business
How AI is helping telcos deal with data complexity
Sean Kennedy , Data and AI Lab Leader at Nokia Bell Labs , tells us how telcos are being transformed with Artificial Intelligence .
AI is helping telcos effectively deal with complexity from data and solve problems very efficiently – at the millisecond speed you need . It is changing the way software is written ; building up the necessary data pipelines is changing software developers ’ fundamental approach . Rather than building fixed algorithms and code bases , we ’ re building models that will learn from the data .
Here are some key use cases for the use of AI for telcos :
AI for predictive hardware maintenance
A key benefit of AI for telcos is predictive hardware maintenance . By following the paradigm that webscalers are already actively using – collecting lots of data and looking for patterns , AI is able to predict the failure of hardware in the future . We often find warning signals as far as 14 days ahead of failure with high confidence . Currently , implementation exists at small scale and we have field trials with major customers in progress .
AI for Self-Organizing Networks
5G networks are becoming increasingly complex . There are more parameters to tune than ever before , there ’ s increased frequency range , an increased number of users to schedule , as well as users that can be scheduled simultaneously . AI machinery is excellent at driving these automations and making them more efficient .
At Nokia , we are currently working on a project which uses AI for Self-Organizing Networks ( SON ). Modern wireless networks have many parameters including for things like power control and energy savings etc . It is extremely difficult for humans to tune these parameters , let alone continuously tune them as your network conditions change , as there is dependency between them all .
AI helps you look at the data and performance values as you ’ re training information and optimize these processes over time automatically using principal techniques , including from mathematics and statistics .
In other words , AI can autonomously learn SON parameters by watching network traffic and radio conversations , using complex mathematical methods such as Bayesian optimization and Markov decision processes . AI outperforms humans at these tasks . Nokia is firmly committed to evolving towards a sustainable world and tools like these will be critical for this evolution .
AI for 5G packet scheduling
Packet scheduling becomes increasingly complex as we move to 5G due to an increase
Sean Kennedy , Data and AI Lab Leader at Nokia
Bell Labs in frequency range , the number of users to schedule , the number of users that can be scheduled simultaneously and the number of ‘ wireless beams ’ used for transmission . Moreover , decisions on how to do this packet scheduling has to happen really fast – in the sub millisecond range – to be useful .
From a mathematical point of view , it is too hard to find the optimal set of transmissions in that time . By going through the data , AI can help us learn the best set of transmissions for a given set of wireless conditions .
AI for Mechanized Inventory
At Nokia we ’ re using AI imaging to determine what is on a job site before technicians arrive to roll out a new network . By using images of equipment and training deep nets to identify different objects , AI removes the strenuous ( and often impossible ) task of keeping inventory up-to-date .
Our research shows a high level of accuracy is possible with deep supervised learning and additional algorithms to automatically adjust images based on differing distances , heights and angles at which images are taken from the same set , be these the back of a rack , a cell tower or other enclosures containing telecom equipment .
AI is already fundamentally changing many industries . For the telco sector , AI is one of the best tools for things like anomaly detection , network optimization and predictive maintenance . We ’ re seeing this across all areas of the stack – from the physical layer all the way up to applications that run on top of the network , even in the deployment of our networks . There is no question that AI is driving improvements across the telco industry , and it will only become more important going forward . p
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