412,817 research outputs found
Seeing the Unobservable: Channel Learning for Wireless Communication Networks
Wireless communication networks rely heavily on channel state information
(CSI) to make informed decision for signal processing and network operations.
However, the traditional CSI acquisition methods is facing many difficulties:
pilot-aided channel training consumes a great deal of channel resources and
reduces the opportunities for energy saving, while location-aided channel
estimation suffers from inaccurate and insufficient location information. In
this paper, we propose a novel channel learning framework, which can tackle
these difficulties by inferring unobservable CSI from the observable one. We
formulate this framework theoretically and illustrate a special case in which
the learnability of the unobservable CSI can be guaranteed. Possible
applications of channel learning are then described, including cell selection
in multi-tier networks, device discovery for device-to-device (D2D)
communications, as well as end-to-end user association for load balancing. We
also propose a neuron-network-based algorithm for the cell selection problem in
multi-tier networks. The performance of this algorithm is evaluated using
geometry-based stochastic channel model (GSCM). In settings with 5 small cells,
the average cell-selection accuracy is 73% - only a 3.9% loss compared with a
location-aided algorithm which requires genuine location information.Comment: 6 pages, 4 figures, accepted by GlobeCom'1
Network Selection for Mobile Nodes in Heterogeneous Wireless Networks using Knapsack Problem Dynamic Algorithms
With the accelerated proliferation wireless networks ranging from GPRS and EDGE to high speed networks such as HSPDA and Mobile Wi-Fi, network selection by mobile nodes will benefit more from knowledge of Network Capability of candidate networks. Network selection is important for handover in heterogeneous wireless environment. User Profiles/Needs and Network Capability will greatly influence the next logical step after network discovery, which is Network Selection. We examine the Dynamic Network Selection paradigm that uses User Profiling/needs to rank networks for selection and ignore networks with less capacity than required, using the Knapsack problem 0/1 Dynamic algorithm and the Knapsack problem Optimization Algorithm
Evaluating DHT-Based Service Placement for Stream Based Overlays
Stream-based overlay networks (SBONs) are one approach to implementing large-scale stream processing systems. A fundamental consideration in an SBON is that of service placement, which determines the physical location of in-network processing services or operators, in such a way that network resources are used efficiently. Service placement consists of two components: node discovery, which selects a candidate
set of nodes on which services might be placed, and node selection, which chooses the particular node to host a service. By viewing the placement problem as the composition of these two processes we can trade-off quality and efficiency between them. A bad discovery scheme can yield a good placement, but at the cost of an expensive selection mechanism.
Recent work on operator placement [3, 9] proposes to leverage routing paths in a distributed hash table (DHT) to obtain a set of candidate nodes for service placement. We evaluate the appropriateness of using DHT routing paths for service placement in an SBON, when aiming to minimize network usage. For this, we consider two DHT-based algorithms for node discovery, which use either the union or intersection of DHT routing paths in the SBON, and compare their performance to other techniques. We show that current DHT-based schemes are actually rather poor node discovery algorithms, when minimizing network utilization. An efficient DHT may not traverse enough hops to obtain a sufficiently large candidate set for placement. The union of DHT routes may result in a low-quality set of discovered nodes that requires an expensive node selection algorithm. Finally, the intersection of DHT routes relies on route convergence, which prevents the placement of services with a large fan-in.Engineering and Applied Science
Intelligent Reward based Data Offloading in Next Generation Vehicular Networks
A massive increase in the number of mobile devices and data hungry vehicular network applications creates a great challenge for Mobile Network Operators (MNOs) to handle huge data in cellular infrastructure. However, due to fluctuating wireless channels and high mobility of vehicular users, it is even more challenging for MNOs to deal with vehicular users within a licensed cellular spectrum. Data offloading in vehicular environment plays a significant role in offloading the vehicle s data traffic from congested cellular network s licensed spectrum to the free unlicensed WiFi spectrum with the help of Road Side Units (RSUs). In this paper, an Intelligent Reward based Data Offloading in Next Generation Vehicular Networks (IR-DON) architecture is proposed for dynamic optimization of data traffic and selection of intelligent RSU. Within IR-DON architecture, an Intelligent Access Network Discovery and Selection Function (I-ANDSF) module with Q-Learning, a reinforcement learning algorithm is designed. I-ANDSF is modeled under Software-Defined Network (SDN) controller to solve the dynamic optimization problem by performing an efficient offloading. This increases the overall system throughput by choosing an optimal and intelligent RSU in the network selection process. Simulation results have shown the accurate network traffic classification, optimal network selection, guaranteed QoS, reduced delay and higher throughput achieved by the I-ANDSF module
Solving the relativistic inverse stellar problem through gravitational waves observation of binary neutron stars
The LIGO/Virgo collaboration has recently announced the direct detection of
gravitational waves emitted in the coalescence of a neutron star binary. This
discovery allows, for the first time, to set new constraints on the behavior of
matter at supranuclear density, complementary with those coming from
astrophysical observations in the electromagnetic band. In this paper we
demonstrate the feasibility of using gravitational signals to solve the
relativistic inverse stellar problem, i.e. to reconstruct the parameters of the
equation of state (EoS) from measurements of the stellar mass and tidal Love
number. We perform Bayesian inference of mock data, based on different models
of the star internal composition, modeled through piecewise polytropes. Our
analysis shows that the detection of a small number of sources by a network of
advanced interferometers would allow to put accurate bounds on the EoS
parameters, and to perform a model selection among the realistic equations of
state proposed in the literature.Comment: minor changes to match the version published on PR
Using a distributed Shapley-value based approach to ensure navigability in a social network of smart objects
The huge number of nodes that is expected to join
the Internet of Things in the short term will add major scalability
issues to several procedures. A recent promising approach to
these issues is based on social networking solutions to allow
objects to autonomously establish social relationships. Every
object in the resulting Social IoT (SIoT) exchanges data with
its friend objects in a distributed manner to avoid the need
for centralized solutions to implement major functionalities,
such as: node discovery, information search and trustworthiness
management. However, the number and types of established
friendship affects network navigability. This paper addresses this
issue proposing an efficient, distributed and dynamic strategy for
the objects to select the right friends for the benefit of the overall
network connectivity. The proposed friendship selection model
relies on a Shapley-value based algorithm mapping the friendship
selection process in the SIoT onto the coalition formation problem
in a corresponding cooperative game. The obtained results show
that the proposed solution is able to ensure global navigability,
measured in terms of average path length among two nodes in
the network, by means of a distributed and wise selection of the
number of friend objects a node has to handle
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