Page 86 - ITU Journal, Future and evolving technologies - Volume 1 (2020), Issue 1, Inaugural issue

ITU Journal on Future and Evolving Technologies, Volume 1 (2020), Issue 1

hill-climb the initial solution detected via any of the API Callback:
described approximate models. At this point, the work- (HSF type, Impinging wave, HSF Configuration R, esulting departing wavefront)
flow is compatible to any modern optimization engine,
Machine-learning based
which receives an input solution and outputs one or estimation
more proposed improvements upon it at each iteration.
Herein, we stress the existence of engines that, also, in- Y Store estimated
Estimation departing wavefront
corporate machine learning mechanisms, to accelerate confidence and calculated
sufficient
the optimization cycle [34]. The optimization can be metrics to DB
based either on full-wave simulations or a real measure- foreach metric
ment test bed, described in Section 6. The optimiza- Analysis-driven N
tion metric can be any reduction of the produced de- Wavefront metric
parting wavefront. Various metrics relevant to antenna Simulations-driven Evaluation type calculation
(reduction)
and propagation theory may be extracted, namely: the
Measurements-driven
number of main lobes (beam directions), the directiv-
ity of main lobes, the side (parasitic) lobes and their Store departing
levels, the beam widths, etc. Such metrics can be used wavefront and (Populate training
calculated metrics data set)
to quantify the metamaterial performance for the re- to DB
quested functionality, e.g. the main lobe directivity and
beam width measures how “well” a metamaterial steers Fig. 10 – Workflow for profiling a metamaterial functionality.
The workflow seeks to produce a data set that describes the
an incoming wavefront to a desired outgoing direction.
metamaterial behavior for any impinging wave type that does not
Lastly, the hill-climbed metamaterial configuration per- match the one specified in the current metamaterial configuration.
taining to the metamaterial API callback is stored into An exhaustive evaluation takes place first for a wide set of possi-
a database for any future use by metamaterial users. ble impinging waves. For intermediate impinging wave cases, the
workflow can rely on estimations produced by machine learning
Finally, we note that multiple simultaneous functionali- algorithms or simple extrapolation means, provided that it yields
ties can be supported by interlacing different scattering an acceptable degree of confidence.
profiles across the metamaterial. In general, this is pre-
always be illuminated by the intended wavefront [24].
formed by spatially mixing the profiles in phasor form
For instance, user mobility can alter the impinging wave-
front in a manner that has limited relation to the in-
N c
X
A mn e jα mn = A c,mn e jα c,mn , (4) tended one and, consequently, to the running metama-
c=1 terial configuration. As such, there is a need for fully
profiling a metamaterial, i.e. calculate and cache its
where c iterates over single, ”low-level” functionalities
expected response for each intended metamaterial con-
and n, m are the unit cell indices. Typically, low-level
figuration, but, also, for each possible (matching or not)
functionalities correspond to simple beam steering op-
impinging wavefront of interest. This profiling process
erations, which are produced exclusively by phase vari-
is outlined in Fig. 10.
ations on the metamaterial (A c,mn = 1). In this case,
The profiling process begins by querying the existing
a ”high-level” functionality will correspond to a multi-
cache (part of the DB) or trained model for the given
splitting operation with variable spatial distribution of
metamaterial and an estimation (or existing calculated
A mn amplitude, raising the hardware requirements for
outcome) of the expected metamaterial response for a
the metamaterial. Therefore, a metamaterial with no
given impinging wave. If it exists, this response is stored
absorption capabilities (and thus no control over A mn )
into a separate profile entry for the metamaterial in the
will have limited access to high-level operations, unless 2
Metamaterial Middleware DB . If the response needs
a mathematical approximation is to be applied, skew-
to be calculated anew, the process proceeds with either
ing the scattering response from its ideal state. As dis-
an analysis-, simulation- or measurement-driven eval-
cussed in Section 6, a method for minimizing amplitude
uation. Therefore, the choice is given as a means to
variations has been successfully investigated by increas-
facilitate the expert into reducing the required compu-
ing the number of secondary parasitic lobes. Such a
tational time, as allowed per case. Then, the profiler
problem can be easily reformulated into an optimization
proceeds to, also, calculate all possible reductions of
task, where an optimal match to the ideal high-level op-
the departing wavefront, e.g. the number of main lobes
eration can be pursued under specific constrains (e.g.
(beam directions), the directivity of the main lobes, the
A mn > const., ∀m, n).
side (parasitic) lobes and their levels, the beam widths,
etc. Finally, once all required impinging wavefronts have
5.2 The Metamaterial Functionality Profiler been successfully processed, the profiling process is con-
The optimization workflow of Fig. 9 opts for the best
2 In case of an estimated response, the user has control over the
metamaterial configuration for a given, specific pair of
process to filter out estimations with low confidence. However,
impinging and departing wavefronts. However, in real the selected estimation engine must be able to provide a confi-
deployments, it is not certain that a metamaterial will dence degree for this automation.
66 © International Telecommunication Union, 2020