To push 5G technology boundaries,
Qualcomm Technologies has been investing
in building advanced end-to-end prototype implementations
of upcoming 5G deployments and designs.
This demonstration showcases the mobility
and performance of 5G NR mmWave operating
in a real-life environment.
This over-the-air test network setup
is located at our headquarters in San Diego,
and it operates in the 28 GHz band
with up to 800 MHz bandwidth.
We have one 5G NR mmWave gNodeB
and 3 remote radio heads, or RRHs.
RRH1 is on top of a 7-story building,
RRH2 is installed on a pole in the lawn area,
and RRH3 is on top of the 3-story garage.
We also have 3 mmWave repeaters that can extend coverage
and improve performance.
The 5G NextGen core network is inside our R&D building
in proximity.
Here is our mobile test device
based on the Qualcomm Snapdragon 5G Modem-RF system.
It has 3 antenna modules that help to overcome blockage
and orientation change,
so that the user can have a uniform experience.
The GUI outlines spectral efficiency or throughput,
depending on the setting, for mmWave downlink and uplink,
as well as the antenna module selection
and associated beam selected for the connection.
In this video, we will compare the performance
of 3 different 5G mmWave repeater implementations.
A simple repeater amplifies
and forwards the signal bidirectionally
and is always active.
A TDD-aware repeater
amplifies and forwards unidirectionally
and is active either in the uplink or downlink direction
depending on the slot structure.
A smart repeater is both TDD-aware
and supports intelligent and dynamic beam-management,
delivering beamformed uplink and downlink
based on the slot structure.
In the first scenario,
the repeater is placed in line-of-sight to RRH3,
but the user moves along a path
that does not have line-of-sight.
Our test results show that all repeater configurations
can provide significant improvement in coverage
and user throughput,
as well as out-to-in coverage.
The smart repeater, however, can provide substantial gains
over simple repeaters.
In this scenario,
we see a user is moving along a path away from RRH3,
but she is in line-of-sight.
As she moves away, the signal strength deteriorates
due to path loss and foliage,
and a repeater is deployed to further extend
line-of-sight coverage.
All three types of repeaters
can deliver adequate range extension,
but a smart repeater can maximize coverage extension
with better beam management.
In this scenario, a user walks into a building
through a glass door;
the repeater can provide mmWave connectivity
from the outdoor RRH3.
While all repeater types can provide range extension,
a simple repeater with a wide-beam
is a good solution in this application
as it is the most cost-effective
and has the simplest implementation.
In this scenario,
the indoor user continues to be served by the outdoor RRH,
as a repeater installed by the glass window
overcomes both out-to-in and diffraction losses.
In this case, a smart repeater can provide
more robust mobile mmWave performance.
Lastly, we are showcasing how repeaters can improve
multi-user multiplexing performance.
One user is served directly by the gNodeB
and the second is served by the repeater.
The smart repeater delivers the best performance here
with intelligent beamforming and reduced interference.
In conclusion, we saw that 5G mmWave repeaters
can cost-effectively improve coverage, mobility, robustness,
and user experience
for a wide range of deployment scenarios.
We look forward to take this technology from our testbed
to real-life networks.
Adopting machine learning for wireless communication
can deliver new benefits,
and this demonstration showcases
how it can enhance 5G mmWave system performance
and efficiency.
This is our mmWave OTA test network in San Diego.
In this test,
we have implemented machine learning based beam prediction
at the gNodeB,
and we are comparing its results to traditional,
non-machine learning based beam selection
in our OTA network.
Here we see several charts on the right side of the screen.
The first chart shows the signal strength captured
from device reports
and comparing it to the ML prediction.
You can see that they are very close to each other,
thereby showing the accuracy of the prediction.
The second chart
shows the beam selection by the OTA system
with and without machine learning.
There are 8 possible beams to choose from,
selected based on channel conditions.
The teal line shows when they match,
which is the desired outcome.
The last chart is another representation
of how well the beams matched.
The diagonal line in teal is the desired outcome,
and it shows the percentage of time matching is achieved
for different beam selections,
and the red combination represents mismatch.
In conclusion, machine learning assisted beam prediction
can reduce the communication overhead of the system,
thereby increasing the usable capacity
and extending device battery life.
As we've shown,
there is still room for potential improvements
for machine learning based beam prediction,
and we will continue to improve our algorithms
in the months to come,
so stay tuned for another update soon.