50,205 research outputs found
Constellation Shaping for WDM systems using 256QAM/1024QAM with Probabilistic Optimization
In this paper, probabilistic shaping is numerically and experimentally
investigated for increasing the transmission reach of wavelength division
multiplexed (WDM) optical communication system employing quadrature amplitude
modulation (QAM). An optimized probability mass function (PMF) of the QAM
symbols is first found from a modified Blahut-Arimoto algorithm for the optical
channel. A turbo coded bit interleaved coded modulation system is then applied,
which relies on many-to-one labeling to achieve the desired PMF, thereby
achieving shaping gain. Pilot symbols at rate at most 2% are used for
synchronization and equalization, making it possible to receive input
constellations as large as 1024QAM. The system is evaluated experimentally on a
10 GBaud, 5 channels WDM setup. The maximum system reach is increased w.r.t.
standard 1024QAM by 20% at input data rate of 4.65 bits/symbol and up to 75% at
5.46 bits/symbol. It is shown that rate adaptation does not require changing of
the modulation format. The performance of the proposed 1024QAM shaped system is
validated on all 5 channels of the WDM signal for selected distances and rates.
Finally, it was shown via EXIT charts and BER analysis that iterative
demapping, while generally beneficial to the system, is not a requirement for
achieving the shaping gain.Comment: 10 pages, 12 figures, Journal of Lightwave Technology, 201
Wireless magnetic sensor network for road traffic monitoring and vehicle classification
Efficiency of transportation of people and goods is playing a vital role in economic growth. A key component for enabling effective planning of transportation networks is the deployment and operation of autonomous monitoring and traffic analysis tools. For that reason, such systems have been developed to register and classify road traffic usage. In this paper, we propose a novel system for road traffic monitoring and classification based on highly energy efficient wireless magnetic sensor networks. We develop novel algorithms for vehicle speed and length estimation and vehicle classification that use multiple magnetic sensors. We also demonstrate that, using such a low-cost system with simplified installation and maintenance compared to current solutions, it is possible to achieve highly accurate estimation and a high rate of positive vehicle classification
Online Learning of Power Transmission Dynamics
We consider the problem of reconstructing the dynamic state matrix of
transmission power grids from time-stamped PMU measurements in the regime of
ambient fluctuations. Using a maximum likelihood based approach, we construct a
family of convex estimators that adapt to the structure of the problem
depending on the available prior information. The proposed method is fully
data-driven and does not assume any knowledge of system parameters. It can be
implemented in near real-time and requires a small amount of data. Our learning
algorithms can be used for model validation and calibration, and can also be
applied to related problems of system stability, detection of forced
oscillations, generation re-dispatch, as well as to the estimation of the
system state.Comment: 7 pages, 4 figure
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