240 research outputs found
Ultra-Reliable Cloud Mobile Computing with Service Composition and Superposition Coding
An emerging requirement for 5G systems is the ability to provide wireless
ultra-reliable communication (URC) services with close-to-full availability for
cloud-based applications. Among such applications, a prominent role is expected
to be played by mobile cloud computing (MCC), that is, by the offloading of
computationally intensive tasks from mobile devices to the cloud. MCC allows
battery-limited devices to run sophisticated applications, such as for gaming
or for the "tactile" internet. This paper proposes to apply the framework of
reliable service composition to the problem of optimal task offloading in MCC
over fading channels, with the aim of providing layered, or composable,
services at differentiated reliability levels. Inter-layer optimization
problems, encompassing offloading decisions and communication resources, are
formulated and addressed by means of successive convex approximation methods.
The numerical results demonstrate the energy savings that can be obtained by a
joint allocation of computing and communication resources, as well as the
advantages of layered coding at the physical layer and the impact of channel
conditions on the offloading decisions.Comment: 8 pages, 5 figures, To be presented at CISS 201
Screening interacting factors in a wireless network testbed using locating arrays
Wireless systems exhibit a wide range of configurable parameters (factors), each with a number of values (levels), that may influence performance. Exhaustively analyzing all factor interactions is typically not feasible in experimental systems due to the large design space. We propose a method for determining which factors play a significant role in wireless network performance with multiple performance metrics (response variables). Such screening can be used to reduce the set of factors in subsequent experimental testing, whether for modelling or optimization. Our method accounts for pairwise interactions between the factors when deciding significance, because interactions play a significant role in real-world systems. We utilize locating arrays to design the experiment because they guarantee that each pairwise interaction impacts a distinct set of tests. We formulate the analysis as a problem in compressive sensing that we solve using a variation of orthogonal matching pursuit, together with statistical methods to determine which factors are significant. We evaluate the method using data collected from the w-iLab.t Zwijnaarde wireless network testbed and construct a new experiment based on the first analysis to validate the results. We find that the analysis exhibits robustness to noise and to missing data
Self-Modeling Based Diagnosis of Software-Defined Networks
Networks built using SDN (Software-Defined Networks) and NFV (Network
Functions Virtualization) approaches are expected to face several challenges
such as scalability, robustness and resiliency. In this paper, we propose a
self-modeling based diagnosis to enable resilient networks in the context of
SDN and NFV. We focus on solving two major problems: On the one hand, we lack
today of a model or template that describes the managed elements in the context
of SDN and NFV. On the other hand, the highly dynamic networks enabled by the
softwarisation require the generation at runtime of a diagnosis model from
which the root causes can be identified. In this paper, we propose finer
granular templates that do not only model network nodes but also their
sub-components for a more detailed diagnosis suitable in the SDN and NFV
context. In addition, we specify and validate a self-modeling based diagnosis
using Bayesian Networks. This approach differs from the state of the art in the
discovery of network and service dependencies at run-time and the building of
the diagnosis model of any SDN infrastructure using our templates
Context Information for Fast Cell Discovery in mm-wave 5G Networks
The exploitation of the mm-wave bands is one of the most promising solutions
for 5G mobile radio networks. However, the use of mm-wave technologies in
cellular networks is not straightforward due to mm-wave harsh propagation
conditions that limit access availability. In order to overcome this obstacle,
hybrid network architectures are being considered where mm-wave small cells can
exploit an overlay coverage layer based on legacy technology. The additional
mm-wave layer can also take advantage of a functional split between control and
user plane, that allows to delegate most of the signaling functions to legacy
base stations and to gather context information from users for resource
optimization. However, mm-wave technology requires high gain antenna systems to
compensate for high path loss and limited power, e.g., through the use of
multiple antennas for high directivity. Directional transmissions must be also
used for the cell discovery and synchronization process, and this can lead to a
non-negligible delay due to the need to scan the cell area with multiple
transmissions at different directions. In this paper, we propose to exploit the
context information related to user position, provided by the separated control
plane, to improve the cell discovery procedure and minimize delay. We
investigate the fundamental trade-offs of the cell discovery process with
directional antennas and the effects of the context information accuracy on its
performance. Numerical results are provided to validate our observations.Comment: 6 pages, 8 figures, in Proceedings of European Wireless 201
On Channel Reciprocity to Activate Uplink Channel Training for Downlink Wireless Transmission in Tactile Internet Applications
We determine, for the first time, the requirement on channel reciprocity to
activate uplink channel training, instead of downlink channel training, to
achieve a higher data rate for the downlink transmission from a multi-antenna
base station to a single-antenna user. We first derive novel closed-form
expressions for the lower bounds on the data rates achieved by the two channel
training strategies by considering the impact of finite blocklength. The
performance comparison result of these two strategies is determined by the
amount of channel reciprocity that is utilized in the uplink channel training.
We then derive an approximated expression for the minimum channel reciprocity
that enables the uplink channel training to outperform the downlink channel
training. Through numerical results, we demonstrate that this minimum channel
reciprocity decreases as the blocklength decreases or the number of transmit
antennas increases, which shows the necessity and benefits of activating the
uplink channel training for short-packet communications with multiple transmit
antennas. This work provides pivotal and unprecedented guidelines on choosing
channel training strategies and channel reciprocity calibrations, offering
valuable insights into latency reduction in the Tactile Internet applications.Comment: 6 pages, 3 figures, Submitted to IEEE ICC 2018 Worksho
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