At Qualcomm Technologies, we're not only leading
the 5G mmWave technology evolution,
but we're also committed
to make mmWave a commercial success.
There are many ways that an operator can go about
deploying mmWave in their 5G networks,
but one common challenge they face
is how to arrive at the most efficient network topology.
Today, I will share with you our R&D effort
in adopting machine learning and optimization techniques
to explore the use of different
mmWave infrastructure options.
Our approach to a more intelligent way
to mmWave network planning involves three steps.
The first is creating a digital twin
of our targeted deployment
based on readily available databases,
such as Google Street View.
Then, we simplify our digital model
and balance design trade-offs, using machine learning based
object recognition, clustering, and pruning.
Last, we evaluate the different
mmWave infrastructure options that can be used
to arrive at the most optimal design
utilizing all the network node types
available to an operator.
Here is a simple graphical representation
of the different mmWave topology options.
We have diverse connectivity options and node types,
such as small cells, integrated access and backhaul,
simple and smart repeater, and more.
Each of these options has pros and cons,
and they can be deployed to meet
different network design objectives.
As an example, we are looking at a small cluster in Tokyo.
Our machine learning algorithm takes open-source
or other available data as an input
and efficiently identifies the different
physical elements that can impact a mmWave deployment,
such as poles, buildings,
foliage and other street furniture that's relevant.
We can perform comprehensive network analysis
to show the cost and performance
of different deployment options.
We use another city, Philadelphia, to illustrate this.
Here we see an example using small cells only,
where a fiber drop is connected
to each new small cell location.
A second deployment option utilizes smart repeaters
and integrated access and backhaul
in additions to small cells, which can simplify deployment,
deliver enhanced performance and/or additional cost-savings.
What we learned is that there is a lot of design versatility
when it comes to deploying mmWave,
and our approach allows us
to quickly weigh the different options.
And of course, we can readily expand this analysis
to a much bigger area.
In conclusion, we are taking a comprehensive approach
to designing and optimizing 5G mmWave networks.
Our approach that combines machine learning
and optimization techniques
could greatly simplify the network planning process.
There is a lot of different tools or node options
in the deployment toolbox,
and our models can be configured to evaluate
different options to meet system requirements.
And lastly, there is an expanded applicability
of this technique to other deployment scenarios
and uses beyond mmWave network planning,
such as for sub-7 GHz and indoor deployments.
While 5G mmWave continues to evolve
bringing better performance and efficiency,
it is also expanding its reach to new device types and tiers.
In Release 17, the Reduced Capability,
or NR-Light Work Item defines a new mmWave
device class that can more efficiently
support the growth of IoT devices while delivering
the wide-band and low-latency benefits of mmWave.
This is a system simulation
of a 5G mmWave network operating in the 26 GHz band
with 400 MHz of bandwidth.
We took Frankfurt, Germany as the basis of our simulations,
where there are 92 mmWave sites in total,
covering an area of approximately 1 square kilometer.
In the baseline scenario,
we only have Release 15 mmWave eMBB devices
enabled in the system,
operating in the entire 400 MHz allocated bandwidth.
As you can see, there are currently 228 devices
taking up approximately 48% of the total network resource.
In the second scenario, a low number of surveillance cameras
were added to the overall simulation.
These are reduced bandwidth NR-Light devices
utilizing 100 MHz of spectrum for uplink communication.
This new mix of 456 devices added 10%
to the overall capacity,
now to a total of 58% network resource.
Now, we add even more NR-Light devices to the system.
We see the number of total NR-Light devices
now going up significantly to 912,
while only increasing overall
network resource utilization to about 68%.
Looking at these three scenarios,
we can conclude that a mmWave network
can efficiently support a large amount
of NR-Light IoT devices, coexisting with eMBB devices
and without significantly impacting network resources.
In this demonstration, we will see how
5G mmWave can support diverse connectivity need
in new verticals.
As an example, we have simulated a 5G mmWave private network
supporting smart factory use cases.
Here is the smart factory floor map,
it's about 34,000 square feet
with a 12-foot ceiling height.
We have 16 mmWave sites covering the space,
including the factory floor and some office spaces.
The mmWave network operates in 28 GHz band
with 800 MHz of bandwidth.
The first type of 5G devices
in the factory is enhanced mobile broadband,
including both smartphones and always-connected laptops.
In this setup, these devices have an average
downlink throughput of over 1100 Mbps.
Boundless extended reality
can enable many emerging Industry 4.0 use cases,
such as guided maintenance and task execution.
To support immersive augmented reality experiences,
heavy processing is done on the edge server
while lighter-load compute can be processed on-device.
With this, boundless XR has a traffic requirement
of 100 Mbps with a 10 ms latency bound
in air interface.
As we can see here, these requirements can be met.
5G can also cut the wire
for extremely high-performance
industrial automation use cases.
Time sensitive networking,
coordinated multipoint, and eURLLC are key 5G capabilities
that can deliver six-nines of reliability
with millisecond-level latency.
As shown here, the mmWave system
can meet these requirements.
In addition to supporting high-performance applications,
mmWave can also scale down
to support lower-complexity IoT devices,
like industrial camera sensors,
using just a 100 MHz bandwidth.
We can see the system supporting a large number of devices
with small impact to network resource utilization.
In conclusion, a 5G mmWave network
can provide seamless connectivity
to a wide range of use cases,
allowing enhanced mobile broadband,
high-performance industrial automation,
and low-complexity IoT use cases to coexist.