At Qualcomm Technologies, we have been driving advancement of cellular air
interface designs for many decades. Looking forward to 5G Advanced and on the path to 6G,
artificial intelligence will play an essential role in solving difficult wireless challenges
and push the boundary of wireless communication. In this video, we will showcase two over-the-air
prototypes that utilize machine learning to bring new levels of air interface performance and efficiency.
Building on last year's system simulation, we have implemented our design of machine learning enabled
air interface in our 5G wide-area test network in San Diego. In this over-the-air demonstration,
we are focusing on how cross-node machine learning can dynamically adapt channel state feedback
for an advanced massive MIMO system. In this implementation, the device sends explicit CSF
back to the gNodeB. The explicit channel feedback framework allows for customized
low overhead feedback based on individual device channel conditions and delivers more efficient
multi-user multiplexing. Here we can see our test network operating in the 3.5 Gigahertz band with
100 Megahertz channel bandwidth. It supports base station with advanced massive MIMO,
along with 5G NextGen core and 5G NR mobile test devices based on the Snapdragon 5G Modem-RF system.
There are neural networks running on both the device and network
which are powered by the Qualcomm Hexagon AI accelerators on mobile and Cloud AI 100 on network.
In this specific implementation, the system hosts multiple neural network models customized for
different channel conditions. In the first scenario, we have a device in the non-line of sight to the
gNodeB. We see that the neural network produces high fidelity channel reconstruction at the base
station and leads to excellent throughput. In the second scenario, we have a line-of-sight device,
the adaptive neural network is shown to deliver high performance while reducing the feedback
overhead. In conclusion, machine learning can bring better performance and efficiency to the 5G system
by exploiting a new data-driven design approach. More importantly, an AI-based dynamic interface
can reap the benefit of quickly evolving neural network technology without waiting for the long
specification update cycle. We are excited to be driving the complementary evolution of 5G
and AI into future standards, as well as real-world end-to-end system implementations.
Adopting machine learning for wireless communication can deliver new benefits
and this demonstration showcases how it can enhance 5G millimeter wave system performance
and efficiency. This is our millimeter wave OTA test network in San Diego.
In this over-the-air demonstration, we have implemented machine learning based
beam management at our gNodeB with three distributed transmission and reception points.
Our test user is carrying the device and roaming in the courtyard area that is fully covered by
the network. First, let's look at how our machine learning algorithm is performing against the
traditional method of beam management. The charts on the right showcase the beam selection by the
OTA system with and without machine learning. There are eight possible beams to choose from,
selected based on channel conditions. The teal line shows when they match, which is the desired outcome.
Here we are comparing the network KPIs with and without machine learning based beam management.
The first chart compares the signal strength captured from device reports to the machine
learning prediction. You can see that they're very close to each other thereby showing the
accuracy of the prediction. This results in very similar achieved throughput numbers as well
as outlined by the second chart. The benefit of machine learning based beam management is realized
through device power savings, which is a result of communication overhead reduction. In conclusion,
machine learning enabled beam management can reduce the communication overhead of the system
thereby increase the usable capacity and extend device battery life. There is a lot of potential
to further enhance 5G system performance and efficiency utilizing machine learning and we will
continue to improve our algorithms in the months to come. So, stay tuned for another update soon.