5 research outputs found
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
Fine-grained performance analysis of massive MTC networks with scheduling and data aggregation
Abstract. The Internet of Things (IoT) represents a substantial shift within wireless communication and constitutes a relevant topic of social, economic, and overall technical impact. It refers to resource-constrained devices communicating without or with low human intervention. However, communication among machines imposes several challenges compared to traditional human type communication (HTC). Moreover, as the number of devices increases exponentially, different network management techniques and technologies are needed. Data aggregation is an efficient approach to handle the congestion introduced by a massive number of machine type devices (MTDs). The aggregators not only collect data but also implement scheduling mechanisms to cope with scarce network resources.
This thesis provides an overview of the most common IoT applications and the network technologies to support them. We describe the most important challenges in machine type communication (MTC). We use a stochastic geometry (SG) tool known as the meta distribution (MD) of the signal-to-interference ratio (SIR), which is the distribution of the conditional SIR distribution given the wireless nodes’ locations, to provide a fine-grained description of the per-link reliability. Specifically, we analyze the performance of two scheduling methods for data aggregation of MTC: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). The results show the fraction of users in the network that achieves a target reliability, which is an important aspect to consider when designing wireless systems with stringent service requirements. Finally, the impact on the fraction of MTDs that communicate with a target reliability when increasing the aggregators density is investigated
Generalized averaged Gaussian quadrature and applications
A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal
MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications
Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described
LIPIcs, Volume 248, ISAAC 2022, Complete Volume
LIPIcs, Volume 248, ISAAC 2022, Complete Volum