684 research outputs found
3GPP-inspired Stochastic Geometry-based Mobility Model for a Drone Cellular Network
This paper deals with the stochastic geometry-based characterization of the
time-varying performance of a drone cellular network in which the initial
locations of drone base stations (DBSs) are modeled as a Poisson point process
(PPP) and each DBS is assumed to move on a straight line in a random direction.
This drone placement and trajectory model closely emulates the one used by the
third generation partnership project (3GPP) for drone-related studies. Assuming
the nearest neighbor association policy for a typical user equipment (UE) on
the ground, we consider two models for the mobility of the serving DBS: (i) UE
independent model, and (ii) UE dependent model. Using displacement theorem from
stochastic geometry, we characterize the time-varying interference field as
seen by the typical UE, using which we derive the time-varying coverage
probability and data rate at the typical UE. We also compare our model with
more sophisticated mobility models where the DBSs may move in nonlinear
trajectories and demonstrate that the coverage probability and rate estimated
by our model act as lower bounds to these more general models. To the best of
our knowledge, this is the first work to perform a rigorous analysis of the
3GPP-inspired drone mobility model and establish connection between this model
and the more general non-linear mobility models
Joint Uplink and Downlink Coverage Analysis of Cellular-based RF-powered IoT Network
Ambient radio frequency (RF) energy harvesting has emerged as a promising
solution for powering small devices and sensors in massive Internet of Things
(IoT) ecosystem due to its ubiquity and cost efficiency. In this paper, we
study joint uplink and downlink coverage of cellular-based ambient RF energy
harvesting IoT where the cellular network is assumed to be the only source of
RF energy. We consider a time division-based approach for power and information
transmission where each time-slot is partitioned into three sub-slots: (i)
charging sub-slot during which the cellular base stations (BSs) act as RF
chargers for the IoT devices, which then use the energy harvested in this
sub-slot for information transmission and/or reception during the remaining two
sub-slots, (ii) downlink sub-slot during which the IoT device receives
information from the associated BS, and (iii) uplink sub-slot during which the
IoT device transmits information to the associated BS. For this setup, we
characterize the joint coverage probability, which is the joint probability of
the events that the typical device harvests sufficient energy in the given time
slot and is under both uplink and downlink signal-to-interference-plus-noise
ratio (SINR) coverage with respect to its associated BS. This metric
significantly generalizes the prior art on energy harvesting communications,
which usually focused on downlink or uplink coverage separately. The key
technical challenge is in handling the correlation between the amount of energy
harvested in the charging sub-slot and the information signal quality (SINR) in
the downlink and uplink sub-slots. Dominant BS-based approach is developed to
derive tight approximation for this joint coverage probability. Several system
design insights including comparison with regularly powered IoT network and
throughput-optimal slot partitioning are also provided
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