1,093 research outputs found
Intracell interference characterization and cluster interference for D2D communication
The homogeneous spatial Poisson point process (SPPP) is widely used for spatial modeling of mobile terminals (MTs). This process is characterized by a homogeneous distribution, complete spatial independence, and constant intensity measure. However, it is intuitive to understand that the locations of MTs are neither homogeneous, due to inhomogeneous terrain, nor independent, due to homophilic relations. Moreover, the intensity is not constant due to mobility. Therefore, assuming an SPPP for spatial modeling is too simplistic, especially for modeling realistic emerging device-centric frameworks such as device-to-device (D2D) communication. In this paper, assuming inhomogeneity, positive spatial correlation, and random intensity measure, we propose a doubly stochastic Poisson process, a generalization of the homogeneous SPPP, to model D2D communication. To this end, we assume a permanental Cox process (PCP) and propose a novel Euler-Characteristic-based approach to approximate the nearest-neighbor distribution function. We also propose a threshold and spatial distances from an excursion set of a chi-square random field as interference control parameters for different cluster sizes. The spatial distance of the clusters is incorporated into a Laplace functional of a PCP to analyze the average coverage probability of a cellular user. A closed-form approximation of the spatial summary statistics is in good agreement with empirical results, and its comparison with an SPPP authenticates the correlation modeling of D2D nodes
Wirelessly Powered Backscatter Communication Networks: Modeling, Coverage and Capacity
Future Internet-of-Things (IoT) will connect billions of small computing
devices embedded in the environment and support their device-to-device (D2D)
communication. Powering this massive number of embedded devices is a key
challenge of designing IoT since batteries increase the devices' form factors
and battery recharging/replacement is difficult. To tackle this challenge, we
propose a novel network architecture that enables D2D communication between
passive nodes by integrating wireless power transfer and backscatter
communication, which is called a wirelessly powered backscatter communication
(WP-BackCom) network. In the network, standalone power beacons (PBs) are
deployed for wirelessly powering nodes by beaming unmodulated carrier signals
to targeted nodes. Provisioned with a backscatter antenna, a node transmits
data to an intended receiver by modulating and reflecting a fraction of a
carrier signal. Such transmission by backscatter consumes orders-of-magnitude
less power than a traditional radio. Thereby, the dense deployment of
low-complexity PBs with high transmission power can power a large-scale IoT. In
this paper, a WP-BackCom network is modeled as a random Poisson cluster process
in the horizontal plane where PBs are Poisson distributed and active ad-hoc
pairs of backscatter communication nodes with fixed separation distances form
random clusters centered at PBs. The backscatter nodes can harvest energy from
and backscatter carrier signals transmitted by PBs. Furthermore, the
transmission power of each node depends on the distance from the associated PB.
Applying stochastic geometry, the network coverage probability and transmission
capacity are derived and optimized as functions of backscatter parameters,
including backscatter duty cycle and reflection coefficient, as well as the PB
density. The effects of the parameters on network performance are
characterized.Comment: 28 pages, 11 figures, has been submitted to IEEE Trans. on Wireless
Communicatio
Series Expansion for Interference in Wireless Networks
The spatial correlations in transmitter node locations introduced by common
multiple access protocols makes the analysis of interference, outage, and other
related metrics in a wireless network extremely difficult. Most works therefore
assume that nodes are distributed either as a Poisson point process (PPP) or a
grid, and utilize the independence properties of the PPP (or the regular
structure of the grid) to analyze interference, outage and other metrics.
But,the independence of node locations makes the PPP a dubious model for
nontrivial MACs which intentionally introduce correlations, e.g. spatial
separation, while the grid is too idealized to model real networks. In this
paper, we introduce a new technique based on the factorial moment expansion of
functionals of point processes to analyze functions of interference, in
particular outage probability. We provide a Taylor-series type expansion of
functions of interference, wherein increasing the number of terms in the series
provides a better approximation at the cost of increased complexity of
computation. Various examples illustrate how this new approach can be used to
find outage probability in both Poisson and non-Poisson wireless networks.Comment: Submitted to IEEE Transactions on Information Theor
Simulating the tail of the interference in a Poisson network model
Interference among simultaneous transmissions represents the main limitation factor for the capacity and connectivity of dense wireless networks. In this paper we provide efficient simulation laws for the tail of the interference in a simple wireless ad hoc network model. Particularly, we consider node locations distributed according to a Poisson point process and various classes of light-tailed fading distribution
Spatial networks with wireless applications
Many networks have nodes located in physical space, with links more common
between closely spaced pairs of nodes. For example, the nodes could be wireless
devices and links communication channels in a wireless mesh network. We
describe recent work involving such networks, considering effects due to the
geometry (convex,non-convex, and fractal), node distribution,
distance-dependent link probability, mobility, directivity and interference.Comment: Review article- an amended version with a new title from the origina
Directionally Convex Ordering of Random Measures, Shot Noise Fields and Some Applications to Wireless Communications
Directionally convex () ordering is a tool for comparison of dependence
structure of random vectors that also takes into account the variability of the
marginal distributions. When extended to random fields it concerns comparison
of all finite dimensional distributions. Viewing locally finite measures as
non-negative fields of measure-values indexed by the bounded Borel subsets of
the space, in this paper we formulate and study the ordering of random
measures on locally compact spaces. We show that the order is preserved
under some of the natural operations considered on random measures and point
processes, such as deterministic displacement of points, independent
superposition and thinning as well as independent, identically distributed
marking. Further operations such as position dependent marking and displacement
of points though do not preserve the order on all point processes, are
shown to preserve the order on Cox point processes. We also examine the impact
of order on the second moment properties, in particular on clustering and
on Palm distributions. Comparisons of Ripley's functions, pair correlation
functions as well as examples seem to indicate that point processes higher in
order cluster more. As the main result, we show that non-negative
integral shot-noise fields with respect to ordered random measures
inherit this ordering from the measures. Numerous applications of this result
are shown, in particular to comparison of various Cox processes and some
performance measures of wireless networks, in both of which shot-noise fields
appear as key ingredients. We also mention a few pertinent open questions.Comment: Accepted in Advances in Applied Probability. Propn. 3.2 strengthened
and as a consequence Cor 6.1,6.2,6.
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