96 research outputs found
On Modeling Coverage and Rate of Random Cellular Networks under Generic Channel Fading
In this paper we provide an analytic framework for computing the expected
downlink coverage probability, and the associated data rate of cellular
networks, where base stations are distributed in a random manner. The provided
expressions are in computable integral forms that accommodate generic channel
fading conditions. We develop these expressions by modelling the cellular
interference using stochastic geometry analysis, then we employ them for
comparing the coverage resulting from various channel fading conditions namely
Rayleigh and Rician fading, in addition to the fading-less channel.
Furthermore, we expand the work to accommodate the effects of random frequency
reuse on the cellular coverage and rate. Monte-Carlo simulations are conducted
to validate the theoretical analysis, where the results show a very close
match
Deep Learning Methods for Device Identification Using Symbols Trace Plot
Devices authentication is one crucial aspect of any communication system.
Recently, the physical layer approach radio frequency (RF) fingerprinting has
gained increased interest as it provides an extra layer of security without
requiring additional components. In this work, we propose an RF fingerprinting
based transmitter authentication approach density trace plot (DTP) to exploit
device-identifiable fingerprints. By considering IQ imbalance solely as the
feature source, DTP can efficiently extract device-identifiable fingerprints
from symbol transition trajectories and density center drifts. In total, three
DTP modalities based on constellation, eye and phase traces are respectively
generated and tested against three deep learning classifiers: the 2D-CNN,
2D-CNN+biLSTM and 3D-CNN. The feasibility of these DTP and classifier pairs is
verified using a practical dataset collected from the ADALM-PLUTO
software-defined radios (SDRs)
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