106 research outputs found
Coexistence of RF-powered IoT and a Primary Wireless Network with Secrecy Guard Zones
This paper studies the secrecy performance of a wireless network (primary
network) overlaid with an ambient RF energy harvesting IoT network (secondary
network). The nodes in the secondary network are assumed to be solely powered
by ambient RF energy harvested from the transmissions of the primary network.
We assume that the secondary nodes can eavesdrop on the primary transmissions
due to which the primary network uses secrecy guard zones. The primary
transmitter goes silent if any secondary receiver is detected within its guard
zone. Using tools from stochastic geometry, we derive the probability of
successful connection of the primary network as well as the probability of
secure communication. Two conditions must be jointly satisfied in order to
ensure successful connection: (i) the SINR at the primary receiver is above a
predefined threshold, and (ii) the primary transmitter is not silent. In order
to ensure secure communication, the SINR value at each of the secondary nodes
should be less than a predefined threshold. Clearly, when more secondary nodes
are deployed, more primary transmitters will remain silent for a given guard
zone radius, thus impacting the amount of energy harvested by the secondary
network. Our results concretely show the existence of an optimal deployment
density for the secondary network that maximizes the density of nodes that are
able to harvest sufficient amount of energy. Furthermore, we show the
dependence of this optimal deployment density on the guard zone radius of the
primary network. In addition, we show that the optimal guard zone radius
selected by the primary network is a function of the deployment density of the
secondary network. This interesting coupling between the two networks is
studied using tools from game theory. Overall, this work is one of the few
concrete works that symbiotically merge tools from stochastic geometry and game
theory
Tight Lower Bounds on the Contact Distance Distribution in Poisson Hole Process
In this letter, we derive new lower bounds on the cumulative distribution
function (CDF) of the contact distance in the Poisson Hole Process (PHP) for
two cases: (i) reference point is selected uniformly at random from
independently of the PHP, and (ii) reference point is located at
the center of a hole selected uniformly at random from the PHP. While one can
derive upper bounds on the CDF of contact distance by simply ignoring the
effect of holes, deriving lower bounds is known to be relatively more
challenging. As a part of our proof, we introduce a tractable way of bounding
the effect of all the holes in a PHP, which can be used to study other
properties of a PHP as well.Comment: To appear in IEEE Wireless Communications Letter
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
Nearest Neighbor and Contact Distance Distribution for Binomial Point Process on Spherical Surfaces
This letter characterizes the statistics of the contact distance and the
nearest neighbor (NN) distance for binomial point processes (BPP)
spatially-distributed on spherical surfaces. We consider a setup of
concentric spheres, with each sphere has a radius and points
that are uniformly distributed on its surface. For that setup, we obtain the
cumulative distribution function (CDF) of the distance to the nearest point
from two types o observation points: (i) the observation point is not a part of
the point process and located on a concentric sphere with a radius
, which corresponds to the contact distance distribution, and
(ii) the observation point belongs to the point process, which corresponds to
the nearest-neighbor (NN) distance distribution
Exploiting Randomly-located Blockages for Large-Scale Deployment of Intelligent Surfaces
One of the promising technologies for the next generation wireless networks
is the reconfigurable intelligent surfaces (RISs). This technology provides
planar surfaces the capability to manipulate the reflected waves of impinging
signals, which leads to a more controllable wireless environment. One potential
use case of such technology is providing indirect line-of-sight (LoS) links
between mobile users and base stations (BSs) which do not have direct LoS
channels. Objects that act as blockages for the communication links, such as
buildings or trees, can be equipped with RISs to enhance the coverage
probability of the cellular network through providing extra indirect LoS-links.
In this paper, we use tools from stochastic geometry to study the effect of
large-scale deployment of RISs on the performance of cellular networks. In
particular, we model the blockages using the line Boolean model. For this
setup, we study how equipping a subset of the blockages with RISs will enhance
the performance of the cellular network. We first derive the ratio of the
blind-spots to the total area. Next, we derive the probability that a typical
mobile user associates with a BS using an RIS. Finally, we derive the
probability distribution of the path-loss between the typical user and its
associated BS. We draw multiple useful system-level insights from the proposed
analysis. For instance, we show that deployment of RISs highly improves the
coverage regions of the BSs. Furthermore, we show that to ensure that the ratio
of blind-spots to the total area is below 10^5, the required density of RISs
increases from just 6 RISs/km2 when the density of the blockages is 300
blockage/km^2 to 490 RISs/km^2 when the density of the blockages is 700
blockage/km^2.Comment: Accepted in IEEE Journal on Selected Areas in Communication
Stochastic Geometry-based Trajectory Design for Multi-Purpose UAVs: Package and Data Delivery
With the advancements achieved in drones' flexibility, low cost, and high
efficiency, they obtain huge application opportunities in various industries,
such as aerial delivery and future communication networks. However, the
increasing transportation needs and expansion of network capacity demands for
UAVs will cause aerial traffic conflicts in the future. To address this issue,
in this paper, we explore the idea of multi-purpose UAVs, which act as aerial
wireless communication data relays and means of aerial transportation
simultaneously to deliver data and packages at the same time. While UAVs
deliver the packages from warehouses to residential areas, we design their
trajectories which enable them to collect data from multiple Internet of Things
(IoT) clusters and forward the collected data to terrestrial base stations
(TBSs). To select the serving nearby IoT clusters, UAVs rank them based on
their priorities and distances. From the perspectives of data and package
delivery, respectively, we propose two algorithms that design the optimal UAVs
trajectory to maximize the transmitted data or minimize the round trip time.
Specifically, we use tools from stochastic geometry to model the locations of
IoT clusters and TBSs. Given the nature of random locations, the proposed
algorithm applies to general cases. Our numerical results show that
multi-purpose UAVs are practical and have great potential to enhance the
energy/time-efficiency of future networks
Stochastic Geometry-Based Low Latency Routing in Massive LEO Satellite Networks
In this paper, the routing in massive low earth orbit (LEO) satellite
networks is studied. When the satellite-to-satellite communication distance is
limited, we choose different relay satellites to minimize the latency in a
constellation at a constant altitude. Firstly, the global optimum solution is
obtained in the ideal scenario when there are available satellites at all the
ideal locations. Next, we propose a nearest neighbor search algorithm for
realistic (non-ideal) scenarios with a limited number of satellites. The
proposed algorithm can approach the global optimum solution under an ideal
scenario through a finite number of iterations and a tiny range of searches.
Compared with other routing strategies, the proposed algorithm shows
significant advantages in terms of latency. Furthermore, we provide two
approximation techniques that can give tight lower and upper bounds for the
latency of the proposed algorithm, respectively. Finally, the relationships
between latency and constellation height, satellites' number, and communication
distance are investigated
On the Influence of Charging Stations Spatial Distribution on Aerial Wireless Networks
Using drones for cellular coverage enhancement is a recent technology that
has shown a great potential in various practical scenarios. However, one of the
main challenges that limits the performance of drone-enabled wireless networks
is the limited flight time. In particular, due to the limited on-board battery
size, the drone needs to frequently interrupt its operation and fly back to a
charging station to recharge/replace its battery. In addition, the charging
station might be responsible to recharge multiple drones. Given that the
charging station has limited capacity, it can only serve a finite number of
drones simultaneously. Hence, in order to accurately capture the influence of
the battery limitation on the performance, it is required to analyze the
dynamics of the time spent by the drones at the charging stations. In this
paper, we use tools from queuing theory and stochastic geometry to study the
influence of each of the charging stations limited capacity and spatial density
on the performance of a drone-enabled wireless network
Coverage analysis of Tethered UAV-assisted Large Scale Cellular Networks
One of the major challenges slowing down the use of unmanned aerial vehicles
(UAVs) as aerial base stations (ABSs) is the limited on-board power supply
which reduces the UAV's flight time. Using a tether to provide UAVs with power
can be considered a reasonable compromise that will enhance the flight time
while limiting the UAV's mobility. In this work, we propose a system where ABSs
are deployed at the centers of user hotspots to offload the traffic and assist
terrestrial base stations (TBSs). Firstly, given the location of the ground
station in the user hotspot (user cluster) and the users spatial distribution,
we compute the optimal inclination angle and length of the tether. Using these
results, we compute the densities of the tethered UAVs deployed at different
altitudes, which enables tractable analysis of the interference in the
considered setup. Next, using tools from stochastic geometry and an approach of
dividing user clusters into finite frames, we analyze the coverage probability
as a function of the maximum tether length, the density of accessible rooftops
for UAV ground station deployment, and the density of clusters. We verify our
findings using Monte-Carlo simulations and draw multiple useful insights. For
instance, we show that it is actually better to deploy UAVs at a fraction of
the clusters, not all of them as it is usually assumed in literature
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