Moving Towards Autonomous Driving Networks
Throughout history, we have never ceased in our pursuit of greater productivity. With each new industrial revolution, from industrialization and digitalization to today’s focus on robotics and artificial intelligence (AI), we have seen giant leaps in terms of our industrial efficiency.
In 1947, US completed the first autonomous transatlantic flight. 1983 witnessed the world’s first driverless metro Métro de Lille going live in France. In 2012, Google obtained the world’s first self-driving car license in Nevada, with its self-driving cars travelling 8 million kilometers as of March 2018. Autonomous driving is no longer the stuff of science fiction. Indeed, today, with the massive strides made in autonomous driving technologies, companies like Tesla are making it possible for people to travel in a comfortable and more environmentally friendly way. In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies.
According to a report by OVUM, over the past decade, growth in the telecommunications industry’s revenue has never outpaced growth in OPEX. As the scale of a network increases, OPEX increases with it, and the structural challenges to an industry come to the fore. Telecom operators depend more heavily on the experience and skills of their staff than OTT players do in network operations and maintenance (O&M). Only 3 engineers are needed for an OTT player to perform O&M on 10,000 devices. But for a telecom operator, that number is 300. Telecom networks also face huge challenges in managing user experience 58% of people’s problems with their home broadband are not identified until they file a complaint. As such, there is now a pressing need for an autonomous driving network.
Unlike autonomous cars, the telecom industry faces unique complexities. In terms of service diversity, a telecom network provides multiple services such as mobile, home broadband, and enterprise services. Therefore, an autonomous driving system must accurately understand the intent behind different services. As for the operating environment and road conditions, there are highways that act like data centers and urban and rural roads that provide broadband access to citizens. Therefore, autonomous driving systems must be able to adapt to complex environments that involve multiple technologies. From the perspective of full lifecycle operations, different roles, such as planning, O&M, and service provisioning, face different challenges.
As an important player in the telecom industry, Huawei has been exploring autonomous driving networks with operators in an attempt to address the structural issues of telecom networks through innovative architecture, helping operators achieve a better service experience and higher operational and resource efficiency.
Autonomous driving networks go far beyond the innovation of a single product, and are more about innovating system architecture and business models. Huawei calls for all industry players to work together to clearly define standards and guide technological innovation and rollout. Based on service experience and operating efficiency, Huawei has proposed five levels of autonomous driving networks for the telecom industry:
L0 manual O&M: The system delivers assisted monitoring capabilities, which means all dynamic tasks have to be executed manually.
L1 assisted O&M: The system executes a certain sub-task based on existing rules to increase execution efficiency.
L2 partial autonomous network: The system enables closed-loop O&M for certain units under certain external environments, lowering the bar for personnel experience and skills.
