928 research outputs found
Robust Distributed Estimation over Multiple Access Channels with Constant Modulus Signaling
A distributed estimation scheme where the sensors transmit with constant
modulus signals over a multiple access channel is considered. The proposed
estimator is shown to be strongly consistent for any sensing noise distribution
in the i.i.d. case both for a per-sensor power constraint, and a total power
constraint. When the distributions of the sensing noise are not identical, a
bound on the variances is shown to establish strong consistency. The estimator
is shown to be asymptotically normal with a variance (AsV) that depends on the
characteristic function of the sensing noise. Optimization of the AsV is
considered with respect to a transmission phase parameter for a variety of
noise distributions exhibiting differing levels of impulsive behavior. The
robustness of the estimator to impulsive sensing noise distributions such as
those with positive excess kurtosis, or those that do not have finite moments
is shown. The proposed estimator is favorably compared with the amplify and
forward scheme under an impulsive noise scenario. The effect of fading is shown
to not affect the consistency of the estimator, but to scale the asymptotic
variance by a constant fading penalty depending on the fading statistics.
Simulations corroborate our analytical results.Comment: 28 pages, 10 figures, submitted to IEEE Transactions on Signal
Processing for consideratio
Performance evaluation of non-prefiltering vs. time reversal prefiltering in distributed and uncoordinated IR-UWB ad-hoc networks
Time Reversal (TR) is a prefiltering scheme mostly analyzed in the context of centralized and synchronous IR-UWB networks, in order to leverage the trade-off between communication performance and device complexity, in particular in presence of multiuser interference. Several strong assumptions have been typically adopted in the analysis of TR, such as the absence of Inter-Symbol / Inter-Frame Interference (ISI/IFI) and multipath dispersion due to complex signal propagation. This work has the main goal of comparing the performance of TR-based systems with traditional non-prefiltered schemes, in the novel context of a distributed and uncoordinated IR-UWB network, under more realistic assumptions including the presence of ISI/IFI and multipath dispersion. Results show that, lack of power control and imperfect channel knowledge affect the performance of both non-prefiltered and TR systems; in these conditions, TR prefiltering still guarantees a performance improvement in sparse/low-loaded and overloaded network scenarios, while the opposite is true for less extreme scenarios, calling for the developement of an adaptive scheme that enables/disables TR prefiltering depending on network conditions
State-of-the-art in Power Line Communications: from the Applications to the Medium
In recent decades, power line communication has attracted considerable
attention from the research community and industry, as well as from regulatory
and standardization bodies. In this article we provide an overview of both
narrowband and broadband systems, covering potential applications, regulatory
and standardization efforts and recent research advancements in channel
characterization, physical layer performance, medium access and higher layer
specifications and evaluations. We also identify areas of current and further
study that will enable the continued success of power line communication
technology.Comment: 19 pages, 12 figures. Accepted for publication, IEEE Journal on
Selected Areas in Communications. Special Issue on Power Line Communications
and its Integration with the Networking Ecosystem. 201
State Estimation of Wireless Sensor Networks in the Presence of Data Packet Drops and Non-Gaussian Noise
Distributed Kalman filter approaches based on the maximum correntropy
criterion have recently demonstrated superior state estimation performance to
that of conventional distributed Kalman filters for wireless sensor networks in
the presence of non-Gaussian impulsive noise. However, these algorithms
currently fail to take account of data packet drops. The present work addresses
this issue by proposing a distributed maximum correntropy Kalman filter that
accounts for data packet drops (i.e., the DMCKF-DPD algorithm). The
effectiveness and feasibility of the algorithm are verified by simulations
conducted in a wireless sensor network with intermittent observations due to
data packet drops under a non-Gaussian noise environment. Moreover, the
computational complexity of the DMCKF-DPD algorithm is demonstrated to be
moderate compared with that of a conventional distributed Kalman filter, and we
provide a sufficient condition to ensure the convergence of the proposed
algorithm
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