Evaluation Engineering - 23

compared with the way things were done
in LTE or previous types of systems. So,
to design those algorithms, it's preferable to have a model of the other components so that you can evaluate the architectural tradeoffs. For example, in the
beamforming space people are looking at
all-digital architectures vs. hybrid digital and analog architectures, and there
are both efficiency and cost reasons to
take different approaches. Fully doing
that type of cross-domain tradeoff isn't
necessarily something which engineers
have been trained on traditionally. The
traditional tools available to them may be
more circuit-oriented and tend to focus
either on RF antenna propagation or DSP
algorithms. How do you bring all those
together? So that is one question we are
answering, and one area we are certainly
investing in is providing a modeling environment where you can put pieces together and get some insights earlier before
you start building prototype hardware.
RN: Is there a hardware-in-the-loop aspect to this, and if so, could you provide
an example?
KK: There is, to some extent. One example
is a demonstration we put together in collaboration with National Instruments,
where we have a model showing DPD
together with a model of the power

amplifier. The intent is not that we are
delivering DPD IP. That is a very specialized task, and our customers want to
create their own that's specific to their
product. Our intent was to show a modeling approach, and in the demonstration,
we took the DPD algorithm and instead
of a model of the power amplifier we put
the algorithm in the loop with some NI
PXI test equipment and an actual hardware power amplifier. We pumped the
test vectors through the test equipment,
it drove the power amplifier, and we got
the results back into MATLAB, where we
could update the coefficients, etc. That's
one HIL example. It provides the capability to validate the implementation vs.
what was predicted in the model.
RN: One expert quoted in my December
report on 5G said there are two types
of challenges with 5G: ones that have
existed for previous cellular generations
and ones that are completely new to 5G.
Would you agree with that?
KK: Yes, certainly. There are several
technical drivers for that. One, I already
mentioned the large bandwidths that actually introduce challenges in terms of
making the RF technology operate in a
linear fashion. The second one is massive
MIMO. It's not happening right now in the
early stages of 5G because Release 15 is

primarily below 6 GHz. [Editor's note: see
the nearby 3GPP 5G Release Timeline.] As
you get into millimeter wave, the massive
MIMO architectures are something that
really hasn't been seen before.
The third one is in the digital baseband
physical layer. The frame structure of 5G
is extremely flexible by intent, so you can
reuse the same structure to achieve high
throughput, high-capacity systems, or you
can dial it down so you have low latency
and lower data rates for IoT and those
types of applications. So, they are anticipating all these different use cases that
have different requirements, and the result was a very flexible but complex frame
structure that's driving up the number
of scenarios or test cases that you need
to evaluate. That definitely is a challenge
both in terms of understanding but also
in terms of the amount of time it takes
actually code and validate the design
and test it.
RN: Once 5G networks have been deployed, is there an ongoing role for
MathWorks to play in evaluations of
quality of experience or coverage?
KK: Yes, I would say so. An early indication
of that is that we have recently introduced
capabilities for visualizing and analyzing
coverage using math-based visualization
of propagation characteristics. So, you're

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Evaluation Engineering

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