Building on many years of foundational
wireless and AI research, we launched the
world's first 5G AI processor last year.
This year, the Snapdragon X75 will
harness AI hardware acceleration to
enable breakthrough 5G performance.
Looking forward, we continue to drive
wireless AI advances that can solve
difficult wireless challenges.
This video showcases the latest
innovations in this key area of our
research for 5G Advanced and beyond.
At Qualcomm Technologies, we have
successfully mobilized 5G millimeter
wave, and it was no small feat. It took
many years of foundational research and
precise engineering to overcome the once
deemed impossible millimeter wave
challenge. An ongoing research area and
an active project in 5G Advanced Release
18 looks at how adopting machine
learning can further improve millimeter wave
performance. The idea is for machine
learning to reliably predict millimeter
wave beam selected for transmission and
reception, thereby reducing signaling
overhead on the network and power
consumption of the device. Our over-the-air
28 Gigahertz test network in San Diego,
California, implements machine learning
based beam management at a base station
and our test user is carrying a 5G
device that also supports machine
learning-based beam Management. In this
test, we are comparing the beam selected
for transmission on the base station
with machine learning based beam
prediction and beam selected by RF
measurements. As you can see in the base
station beam selection charts, they match
in most instances. This is confirmed by
the signal strength and throughput
charts, and this is the desired outcome.
Now let's look at the device side, which
also implements machine learning based
beam prediction. Similarly, we see the
throughput obtained with machine
learning prediction matches well with
non-machine learning reference. The
benefit is realized through device power
savings, which is a result of communication
overhead reduction. In conclusion, we saw
how machine learning enabled beam
management on both the base station and
devices can further improve millimeter
wave system performance. We're very
excited about the potential of wireless
machine learning and how it can help to
improve all aspects of wireless
communications and more.
5G Advanced started the journey of
utilizing machine learning as a tool for
wireless system design. And one focused
area is a data-driven air interface that
can reap the benefits of cross-node
machine learning between the network and
devices. One specific use case being
studied in Release 18 is to dynamically
adapt Channel State Feedback for the
advanced massive MIMO system. While we
have shown the benefits of such design
in our demonstration from previous years,
there still exists the implementation
challenge when multiple vendors are
involved. Let me explain. For this
demonstration, we are utilizing our
improved 3.5 Gigahertz test network in
San Diego, California. In this first
scenario, we have a multi-vendor
implementation, which means the network
equipment and devices come from
different vendors. While it is possible to
train the encoder and decoder neural
networks independently, currently defined
in 3GPP, it does not yield ideal results.
And to facilitate joint training, which
is to train the base station and device
simultaneously, it requires sharing of
proprietary knowledge of neural network
structures across vendors. This is not
desirable. To avoid this, we can adopt a
sequential training methodology.
In this implementation, device and base
station vendors agree on data sharing
for sequential training, and new network
structures do not need to be shared
across vendors. In our example, the device
is trained first from the data it
collected, and the data is then shared
with the base station for training. The
result is comparable system performance
as shown in the graphs.
But sequential training can enable
multiple network and device vendors in
the same system. A data-driven air
interface can dynamically and
continuously learn from the environment
to improve its performance and
efficiency. In conclusion, we see that
multi-vendor cross-node machine learning
can be practically implemented through
sequential training. We are excited to be
driving the complementary 5G and AI
evolution into future standards as well
as towards real world system
implementations.
If you want to see another research area,
click here.
If you want to learn more about what we
are showing at MWC, click here.
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