349 research outputs found
Simple and accurate analytical model of planar grids and high-impedance surfaces comprising metal strips or patches
This paper introduces simple analytical formulas for the grid impedance of
electrically dense arrays of square patches and for the surface impedance of
high-impedance surfaces based on the dense arrays of metal strips or square
patches over ground planes. Emphasis is on the oblique-incidence excitation.
The approach is based on the known analytical models for strip grids combined
with the approximate Babinet principle for planar grids located at a dielectric
interface. Analytical expressions for the surface impedance and reflection
coefficient resulting from our analysis are thoroughly verified by full-wave
simulations and compared with available data in open literature for particular
cases. The results can be used in the design of various antennas and microwave
or millimeter wave devices which use artificial impedance surfaces and
artificial magnetic conductors (reflect-array antennas, tunable phase shifters,
etc.), as well as for the derivation of accurate higher-order impedance
boundary conditions for artificial (high-) impedance surfaces. As an example,
the propagation properties of surface waves along the high-impedance surfaces
are studied.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Antennas and
Propagatio
Radio Frequency Fingerprinting Exploiting Non-Linear Memory Effect
Radio frequency fingerprint (RFF) identification distinguishes wireless transmitters by exploiting their hardware imperfection that is inherent in typical radio frequency (RF) front ends. This can reduce the risks for the identities of legitimate devices being copied, or forged, which can also occur in conventional software-based identification systems. This paper analyzes the feasibility of device identification exploiting the unique non-linear memory effect of the transmitter RF chains consisting of matched pulse shaping filters and non-linear power amplifiers (PAs). This unique feature can be extracted from the received distorted constellation diagrams (CDs) with the help of image recognition-based classification algorithms. In order to validate the performance of the proposed RFF approach, experiments are carried out in cabled and over the air (OTA) scenarios. In the cabled experiment, the average classification accuracy among systems of 8 PAs (4 PAs of the same model and the other 4 of different models) is around 92% at signal to noise ratio (SNR) of 10 dB. For the OTA line-of-sight (LOS) scenario, the average classification accuracy is 90% at SNR of 10 dB; for the non-line-of-sight (NLOS) scenario, the average classification accuracy is 79% at SNR of 12 dB
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