973 research outputs found
Filter-And-Forward Distributed Beamforming in Relay Networks with Frequency Selective Fading
A new approach to distributed cooperative beamforming in relay networks with
frequency selective fading is proposed. It is assumed that all the relay nodes
are equipped with finite impulse response (FIR) filters and use a
filter-and-forward (FF) strategy to compensate for the transmitter-to-relay and
relay-to-destination channels.
Three relevant half-duplex distributed beamforming problems are considered.
The first problem amounts to minimizing the total relay transmitted power
subject to the destination quality-of-service (QoS) constraint. In the second
and third problems, the destination QoS is maximized subject to the total and
individual relay transmitted power constraints, respectively. For the first and
second problems, closed-form solutions are obtained, whereas the third problem
is solved using convex optimization. The latter convex optimization technique
can be also directly extended to the case when the individual and total power
constraints should be jointly taken into account. Simulation results
demonstrate that in the frequency selective fading case, the proposed FF
approach provides substantial performance improvements as compared to the
commonly used amplify-and-forward (AF) relay beamforming strategy.Comment: Submitted to IEEE Trans. on Signal Processing on 8 July 200
Stochastic Acceleration of Low Energy Electrons in Plasmas with Finite Temperature
This paper extends our earlier work on the acceleration of low-energy
electrons by plasma turbulence to include the effects of finite temperature of
the plasma. We consider the resonant interaction of thermal electrons with the
whole transverse branch of plasma waves propagating along the magnetic field.
We show that our earlier published results for acceleration of low-energy
electrons can be applied to the case of finite temperature if a sufficient
level of turbulence is present. From comparison of the acceleration rate of the
thermal particles with the decay rate of the waves with which they interact, we
determine the required energy density of the waves as a fraction of the
magnetic energy density, so that a substantial fraction of the background
plasma electrons can be accelerated. The dependence of this value on the plasma
parameter alpha = omega_pe / Omega_e (the ratio of electron plasma frequency to
electron gyrofrequency), plasma temperature, and turbulence spectral parameters
is quantified. We show that the result is most sensitive to the plasma
parameter alpha. We estimate the required level of total turbulence by
calculating the level of turbulence required for the initial acceleration of
thermal electrons and that required for further acceleration to higher
energies
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Perceptual multistability as Markov Chain Monte Carlo inference
While many perceptual and cognitive phenomena are well described in terms of Bayesian inference, the necessary computations are intractable at the scale of real-world tasks, and it remains unclear how the human mind approximates Bayesian computations algorithmically. We explore the proposal that for some tasks, humans use a form of Markov Chain Monte Carlo to approximate the posterior distribution over hidden variables. As a case study, we show how several phenomena of perceptual multistability can be explained as MCMC inference in simple graphical models for low-level vision
Moving Target Parameters Estimation in Non-Coherent MIMO Radar Systems
The problem of estimating the parameters of a moving target in multiple-input
multiple-output (MIMO) radar is considered and a new approach for estimating
the moving target parameters by making use of the phase information associated
with each transmit-receive path is introduced. It is required for this
technique that different receive antennas have the same time reference, but no
synchronization of initial phases of the receive antennas is needed and,
therefore, the estimation process is non-coherent. We model the target motion
within a certain processing interval as a polynomial of general order. The
first three coefficients of such a polynomial correspond to the initial
location, velocity, and acceleration of the target, respectively. A new maximum
likelihood (ML) technique for estimating the target motion coefficients is
developed. It is shown that the considered ML problem can be interpreted as the
classic "overdetermined" nonlinear least-squares problem. The proposed ML
estimator requires multi-dimensional search over the unknown polynomial
coefficients. The Cram\'er-Rao Bound (CRB) for the proposed parameter
estimation problem is derived. The performance of the proposed estimator is
validated by simulation results and is shown to achieve the CRB.Comment: 17 pages, 4 figures, Submitted to the IEEE Trans. Signal Processing
in Aug. 201
Spacecraft observations and analytic theory of crescent-shaped electron distributions in asymmetric magnetic reconnection
Supported by a kinetic simulation, we derive an exclusion energy parameter
providing a lower kinetic energy bound for an electron to cross
from one inflow region to the other during magnetic reconnection. As by a
Maxwell Demon, only high energy electrons are permitted to cross the inner
reconnection region, setting the electron distribution function observed along
the low density side separatrix during asymmetric reconnection. The analytic
model accounts for the two distinct flavors of crescent-shaped electron
distributions observed by spacecraft in a thin boundary layer along the low
density separatrix.Comment: 6 pages, 3 figure
Cooperative Transmission for Wireless Relay Networks Using Limited Feedback
To achieve the available performance gains in half-duplex wireless relay
networks, several cooperative schemes have been earlier proposed using either
distributed space-time coding or distributed beamforming for the transmitter
without and with channel state information (CSI), respectively. However, these
schemes typically have rather high implementation and/or decoding complexities,
especially when the number of relays is high. In this paper, we propose a
simple low-rate feedback-based approach to achieve maximum diversity with a low
decoding and implementation complexity. To further improve the performance of
the proposed scheme, the knowledge of the second-order channel statistics is
exploited to design long-term power loading through maximizing the receiver
signal-to-noise ratio (SNR) with appropriate constraints. This maximization
problem is approximated by a convex feasibility problem whose solution is shown
to be close to the optimal one in terms of the error probability. Subsequently,
to provide robustness against feedback errors and further decrease the feedback
rate, an extended version of the distributed Alamouti code is proposed. It is
also shown that our scheme can be generalized to the differential transmission
case, where it can be applied to wireless relay networks with no CSI available
at the receiver.Comment: V1: 27 pages, 1 column, 6 figures. Submitted to IEEE Transactions on
Signal Processing, February 2, 2009. V2: 30 pages, 1 column, 8 figures.
Revised version submitted to IEEE Transactions on Signal Processing, July 23,
200
Reinstated episodic context guides sampling-based decisions for reward.
How does experience inform decisions? In episodic sampling, decisions are guided by a few episodic memories of past choices. This process can yield choice patterns similar to model-free reinforcement learning; however, samples can vary from trial to trial, causing decisions to vary. Here we show that context retrieved during episodic sampling can cause choice behavior to deviate sharply from the predictions of reinforcement learning. Specifically, we show that, when a given memory is sampled, choices (in the present) are influenced by the properties of other decisions made in the same context as the sampled event. This effect is mediated by fMRI measures of context retrieval on each trial, suggesting a mechanism whereby cues trigger retrieval of context, which then triggers retrieval of other decisions from that context. This result establishes a new avenue by which experience can guide choice and, as such, has broad implications for the study of decisions
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