With an increase in
smartphone penetration, the democratization of internet, HD video consumption and the use of sophisticated technologies like AR and VR, massive amounts of data
constantly hit a mobile network today. According to the Ericsson Mobility The report, June 2019 edition, data traffic per smartphone per month in South East
Asia and Oceania will grow from 3.6GB to 17GB at a compounded annual growth
rate of 29%. The report also predicts that the total mobile data traffic per
month in the region is expected to grow 7 times from 2.3EB in 2018 to 16EB by
2024. With 5G and rapid expansion of IoT devices, data traffic will only grow
further, prompting legacy networks to evolve into dynamic ones that are able to
react in real-time to increased demands, problems, and shifts in traffic.
Data deluge with 5G
The Ericsson Mobility report highlights that the total mobile
traffic is expected to reach 136EB/month by the end of 2024. In the same time
period, the number of cellular IoT connections is forecasted to reach 4.1
billion. As a result, network congestion will increase as service providers
will handle multiple technologies such as 4G, 5G and IoT in tandem. Therefore,
it is no wonder that service providers are gearing up to deploy #artificial intelligence
and #machine_learning to manage the complexity and optimize system performance.
With 5G driven applications coming into play, networks will experience improved
speed, consistency, reliability and capacity. To manage these factors, there
will be a demand for faster, more responsive, available-on-demand networks that
will be possible only through #AI and #machine_learning deployment. Critical
areas where service providers are already seeing the value and return from #AI are
in building new revenue opportunities and enjoying operational cost savings. As
they scale up their transformation, the benefits of automation and #AI
solutions deployment in networks become more evident.
Transforming to networks of tomorrow
According to an Ericsson, a whitepaper on employing #AI techniques,
91 percent of service providers in Southeast Asia, India and Oceania want more
#AI in their network as they believe it will serve as an essential component
for handling the increased traffic and other complexities. One of the foremost
advantages of using machine learning in networks are that it will analyze raw
data With an increase in smartphone
penetration, the democratization of internet, HD video consumption and use of
sophisticated technologies like AR and VR, massive amounts of data constantly
hit a mobile network today. According to the Ericsson Mobility Report, June
2019 edition, data traffic per smartphone per month in South East Asia and
Oceania will grow from 3.6GB to 17GB at a compounded annual growth rate of 29%.
The report also predicts that the total mobile data traffic per month in the region is expected to grow 7 times from 2.3EB in 2018 to 16EB by 2024. With 5G
and rapid expansion of IoT devices, data traffic will only grow further,
prompting legacy networks to evolve into dynamic ones that are able to react in
real-time to increased demands, problems, and shifts in traffic.
Data deluge with 5G
The Ericsson Mobility report highlights that the total mobile
traffic is expected to reach 136EB/month by the end of 2024. In the same time
period, the number of cellular IoT connections is forecasted to reach 4.1
billion. As a result, network congestion will increase as service providers
will handle multiple technologies such as 4G, 5G and IoT in tandem. Therefore,
it is no wonder that service providers are gearing up to deploy #artificial intelligence
and #machine_learning to manage the complexity and optimize system performance.
With 5G driven applications coming into play, networks will experience improved
speed, consistency, reliability and capacity. To manage these factors, there
will be a demand for faster, more responsive, available-on-demand networks that
will be possible only through #AI and #machine_learning deployment. Critical
areas where service providers are already seeing the value and return from #AI are
in building new revenue opportunities and enjoying operational cost savings. As
they scale up their transformation, the benefits of automation and #AI
solutions deployment in networks become more evident.
Transforming to networks of tomorrow
According to an Ericsson, a whitepaper on employing #AI techniques,
91 percent of service providers in Southeast Asia, India and Oceania want more
#AI in their network as they believe it will serve as an essential component
for handling the increased traffic and other complexities. One of the foremost
advantages of using machine learning in networks are that it will analyze raw
data and will be able to yield further insights. By building cognitive and
predictive #AI algorithms, operators will be able to effectively manage network
traffic and ensure network performance has minimal to zero impact with
additional devices. AI-enabled networks will employ advanced data analytics
that will make systems smart, adaptive, self-aware, proactive and prescriptive.
Ultimately, increasing the use of AI in managing networks will play a key role in
reducing associated operating costs and in addressing many of the barriers that
service providers have indicated are preventing insights from data being acted
upon.
The end consumers immensely stand to gain from improved network
capabilities as well. For instance, 4K video has seen an unprecedented rise in
popularity among viewers who now want to experience the same level of detail
and quality on the go. On 5G networks, AI and automation will help predict lags
in-service experiences and automate fixes, allowing consumers to enjoy a
seamless 4K consumption. Leveraging on its developments in Artificial
Intelligence (AI) and automation, Ericsson recently announced that it will
support Airtel in India to proactively address network complexity and boost
user experience. Combining deep domain expertise with advanced technologies
like AI and automation, Ericsson managed services provide the performance,
reliability, and flexibility to meet the dynamic needs of consumers and
enterprises as well as intelligently monitoring and managing networks to drive
operational efficiencies.
Given the increasing complexity and
scale of data volumes that 5G will bring in, manual operations by human workers
will need to be augmented .The good news is that operators have already started
taking steps towards AI application and enhancing network performance to
maximise end customer satisfaction. In order to provide a rich and seamless
experience for both industrial partners and end consumers, it is important to
speed up adoption of AI, automation and machine learning. It will help service
providers to operate in a resilient and secure manner and take the mobile
network to a new level of innovation for the benefit of industry and society.
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