110 research outputs found
CapEst: A Measurement-based Approach to Estimating Link Capacity in Wireless Networks
Estimating link capacity in a wireless network is a complex task because the
available capacity at a link is a function of not only the current arrival rate
at that link, but also of the arrival rate at links which interfere with that
link as well as of the nature of interference between these links. Models which
accurately characterize this dependence are either too computationally complex
to be useful or lack accuracy. Further, they have a high implementation
overhead and make restrictive assumptions, which makes them inapplicable to
real networks.
In this paper, we propose CapEst, a general, simple yet accurate,
measurement-based approach to estimating link capacity in a wireless network.
To be computationally light, CapEst allows inaccuracy in estimation; however,
using measurements, it can correct this inaccuracy in an iterative fashion and
converge to the correct estimate. Our evaluation shows that CapEst always
converged to within 5% of the correct value in less than 18 iterations. CapEst
is model-independent, hence, is applicable to any MAC/PHY layer and works with
auto-rate adaptation. Moreover, it has a low implementation overhead, can be
used with any application which requires an estimate of residual capacity on a
wireless link and can be implemented completely at the network layer without
any support from the underlying chipset
AirSync: Enabling Distributed Multiuser MIMO with Full Spatial Multiplexing
The enormous success of advanced wireless devices is pushing the demand for
higher wireless data rates. Denser spectrum reuse through the deployment of
more access points per square mile has the potential to successfully meet the
increasing demand for more bandwidth. In theory, the best approach to density
increase is via distributed multiuser MIMO, where several access points are
connected to a central server and operate as a large distributed multi-antenna
access point, ensuring that all transmitted signal power serves the purpose of
data transmission, rather than creating "interference." In practice, while
enterprise networks offer a natural setup in which distributed MIMO might be
possible, there are serious implementation difficulties, the primary one being
the need to eliminate phase and timing offsets between the jointly coordinated
access points.
In this paper we propose AirSync, a novel scheme which provides not only time
but also phase synchronization, thus enabling distributed MIMO with full
spatial multiplexing gains. AirSync locks the phase of all access points using
a common reference broadcasted over the air in conjunction with a Kalman filter
which closely tracks the phase drift. We have implemented AirSync as a digital
circuit in the FPGA of the WARP radio platform. Our experimental testbed,
comprised of two access points and two clients, shows that AirSync is able to
achieve phase synchronization within a few degrees, and allows the system to
nearly achieve the theoretical optimal multiplexing gain. We also discuss MAC
and higher layer aspects of a practical deployment. To the best of our
knowledge, AirSync offers the first ever realization of the full multiuser MIMO
gain, namely the ability to increase the number of wireless clients linearly
with the number of jointly coordinated access points, without reducing the per
client rate.Comment: Submitted to Transactions on Networkin
A Utility-Preserving Obfuscation Approach for YouTube Recommendations
Online content platforms optimize engagement by providing personalized
recommendations to their users. These recommendation systems track and profile
users to predict relevant content a user is likely interested in. While the
personalized recommendations provide utility to users, the tracking and
profiling that enables them poses a privacy issue because the platform might
infer potentially sensitive user interests. There is increasing interest in
building privacy-enhancing obfuscation approaches that do not rely on
cooperation from online content platforms. However, existing obfuscation
approaches primarily focus on enhancing privacy but at the same time they
degrade the utility because obfuscation introduces unrelated recommendations.
We design and implement De-Harpo, an obfuscation approach for YouTube's
recommendation system that not only obfuscates a user's video watch history to
protect privacy but then also denoises the video recommendations by YouTube to
preserve their utility. In contrast to prior obfuscation approaches, De-Harpo
adds a denoiser that makes use of a "secret" input (i.e., a user's actual watch
history) as well as information that is also available to the adversarial
recommendation system (i.e., obfuscated watch history and corresponding "noisy"
recommendations). Our large-scale evaluation of De-Harpo shows that it
outperforms the state-of-the-art by a factor of 2x in terms of preserving
utility for the same level of privacy, while maintaining stealthiness and
robustness to de-obfuscation
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