120,861 research outputs found
Data Dissemination Performance in Large-Scale Sensor Networks
As the use of wireless sensor networks increases, the need for
(energy-)efficient and reliable broadcasting algorithms grows. Ideally, a
broadcasting algorithm should have the ability to quickly disseminate data,
while keeping the number of transmissions low. In this paper we develop a model
describing the message count in large-scale wireless sensor networks. We focus
our attention on the popular Trickle algorithm, which has been proposed as a
suitable communication protocol for code maintenance and propagation in
wireless sensor networks. Besides providing a mathematical analysis of the
algorithm, we propose a generalized version of Trickle, with an additional
parameter defining the length of a listen-only period. This generalization
proves to be useful for optimizing the design and usage of the algorithm. For
single-cell networks we show how the message count increases with the size of
the network and how this depends on the Trickle parameters. Furthermore, we
derive distributions of inter-broadcasting times and investigate their
asymptotic behavior. Our results prove conjectures made in the literature
concerning the effect of a listen-only period. Additionally, we develop an
approximation for the expected number of transmissions in multi-cell networks.
All results are validated by simulations
Energy-Efficient Design of Satellite-Terrestrial Computing in 6G Wireless Networks
In this paper, we investigate the issue of satellite-terrestrial computing in
the sixth generation (6G) wireless networks, where multiple terrestrial base
stations (BSs) and low earth orbit (LEO) satellites collaboratively provide
edge computing services to ground user equipments (GUEs) and space user
equipments (SUEs) over the world. In particular, we design a complete process
of satellite-terrestrial computing in terms of communication and computing
according to the characteristics of 6G wireless networks. In order to minimize
the weighted total energy consumption while ensuring delay requirements of
computing tasks, an energy-efficient satellite-terrestrial computing algorithm
is put forward by jointly optimizing offloading selection, beamforming design
and resource allocation. Finally, both theoretical analysis and simulation
results confirm fast convergence and superior performance of the proposed
algorithm for satellite-terrestrial computing in 6G wireless networks
Design and analysis of distributed utility maximization algorithm for multihop wireless network with inaccurate feedback
Distributed network utility maximization (NUM) is receiving increasing interests for cross-layer optimization problems in multihop wireless networks. Traditional distributed NUM algorithms rely heavily on feedback information between different network elements, such as traffic sources and routers. Because of the distinct features of multihop wireless networks such as time-varying channels and dynamic network topology, the feedback information is usually inaccurate, which represents as a major obstacle for distributed NUM application to wireless networks. The questions to be answered include if distributed NUM algorithm can converge with inaccurate feedback and how to design effective distributed NUM algorithm for wireless networks. In this paper, we first use the infinitesimal perturbation analysis technique to provide an unbiased gradient estimation on the aggregate rate of traffic sources at the routers based on locally available information. On the basis of that, we propose a stochastic approximation algorithm to solve the distributed NUM problem with inaccurate feedback. We then prove that the proposed algorithm can converge to the optimum solution of distributed NUM with perfect feedback under certain conditions. The proposed algorithm is applied to the joint rate and media access control problem for wireless networks. Numerical results demonstrate the convergence of the proposed algorithm
Waveform Optimization for Large-Scale Multi-Antenna Multi-Sine Wireless Power Transfer
Wireless power transfer (WPT) is expected to be a technology reshaping the
landscape of low-power applications such as the Internet of Things,
machine-to-machine communications and radio frequency identification networks.
Although there has been some progress towards multi-antenna multi-sine WPT
design, the large-scale design of WPT, reminiscent of massive multiple-input
multiple-output (MIMO) in communications, remains an open problem. Considering
the nonlinear rectifier model, a multiuser waveform optimization algorithm is
derived based on successive convex approximation (SCA). A lower-complexity
algorithm is derived based on asymptotic analysis and sequential approximation
(SA). It is shown that the difference between the average output voltage
achieved by the two algorithms can be negligible provided the number of
antennas is large enough. The performance gain of the nonlinear model based
design over the linear model based design can be large, in the presence of a
large number of tones.Comment: To appear in the 17th IEEE International Workshop on Signal
Processing Advances in Wireless Communications (SPAWC 2016
Performance analysis of large wireless networks: a stochastic geometry approach
In recent years, stochastic geometry has emerged as a powerful tool for the modeling, analysis, and design of wireless networks with random topologies. Stochastic geometry has been demonstrated to provide a tractable yet an accurate approach for the performance analysis of wireless networks, when the network nodes are modeled as a Poisson point process. This thesis develops analytical frameworks to study the performance of various large-scale wireless networks with random topologies. Firstly, it develops a mathematical model for the uplink analysis of heterogeneous cellular networks when the base stations have multiple antennas. Further, it studies how the gains of downlink and uplink decoupling can be optimized in such a network. Secondly, this thesis also models, analyzes, and designs an ad-hoc network architecture that utilizes both the wireless power transfer and backscatter communications. The performance of such a network is further compared with a regular powered network. Finally, this thesis for the first time develops a scheduling algorithm for cellular networks that has an information theoretic justification. Then using tools from stochastic geometry, this thesis quantifies the gains of such scheduling algorithm
over the traditional scheduling algorithm for the downlink transmission. Furthermore, to find the optimal system parameters that provide the maximum gains, this thesis performs asymptotic analysis and provides a simple optimization algorithm. The accuracy of all the mathematical models have been verified with extensive Monte Carlo simulations.Open Acces
Throughput-Optimal Broadcast on Directed Acyclic Graphs
We study the problem of broadcasting packets in wireless networks. At each
time slot, a network controller activates non-interfering links and forwards
packets to all nodes at a common rate; the maximum rate is referred to as the
broadcast capacity of the wireless network. Existing policies achieve the
broadcast capacity by balancing traffic over a set of spanning trees, which are
difficult to maintain in a large and time-varying wireless network. We propose
a new dynamic algorithm that achieves the broadcast capacity when the
underlying network topology is a directed acyclic graph (DAG). This algorithm
utilizes local queue-length information, does not use any global topological
structures such as spanning trees, and uses the idea of in-order packet
delivery to all network nodes. Although the in-order packet delivery constraint
leads to degraded throughput in cyclic graphs, we show that it is throughput
optimal in DAGs and can be exploited to simplify the design and analysis of
optimal algorithms. Our simulation results show that the proposed algorithm has
superior delay performance as compared to tree-based approaches.Comment: To appear in the proceedings of INFOCOM, 201
Communication Primitives in Cognitive Radio Networks
Cognitive radio networks are a new type of multi-channel wireless network in
which different nodes can have access to different sets of channels. By
providing multiple channels, they improve the efficiency and reliability of
wireless communication. However, the heterogeneous nature of cognitive radio
networks also brings new challenges to the design and analysis of distributed
algorithms.
In this paper, we focus on two fundamental problems in cognitive radio
networks: neighbor discovery, and global broadcast. We consider a network
containing nodes, each of which has access to channels. We assume the
network has diameter , and each pair of neighbors have at least ,
and at most , shared channels. We also assume each node has at
most neighbors. For the neighbor discovery problem, we design a
randomized algorithm CSeek which has time complexity
. CSeek is flexible and robust,
which allows us to use it as a generic "filter" to find "well-connected"
neighbors with an even shorter running time. We then move on to the global
broadcast problem, and propose CGCast, a randomized algorithm which takes
time. CGCast uses
CSeek to achieve communication among neighbors, and uses edge coloring to
establish an efficient schedule for fast message dissemination.
Towards the end of the paper, we give lower bounds for solving the two
problems. These lower bounds demonstrate that in many situations, CSeek and
CGCast are near optimal
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