25,653 research outputs found
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Accuracy of numerical solutions using the eulers equation residuals
In this paper we derive sorne asymptotic properties on the accuracy of numerical solutions. We sIlow tIlat the approximation error of the policy function is of the same order of magnitude as the size of the Euler equation residuals. Moreover, for bounding this approximation error tIle most relevant parameters are the discount factor and the curvature of the return function. These findings provide theoretical foundations for the construction of tests that can assess the performance of alternative computational methods
Asymptotic Signal Detection Rates with 1-bit Array Measurements
This work considers detecting the presence of a band-limited random radio
source using an antenna array featuring a low-complexity digitization process
with single-bit output resolution. In contrast to high-resolution
analog-to-digital conversion, such a direct transformation of the analog radio
measurements to a binary representation can be implemented hardware and
energy-efficient. However, the probabilistic model of the binary receive data
becomes challenging. Therefore, we first consider the Neyman-Pearson test
within generic exponential families and derive the associated analytic
detection rate expressions. Then we use a specific replacement model for the
binary likelihood and study the achievable detection performance with 1- bit
radio array measurements. As an application, we explore the capability of a
low-complexity GPS spectrum monitoring system with different numbers of
antennas and different observation intervals. Results show that with a moderate
amount of binary sensors it is possible to reliably perform the monitoring
task
On the policy function in continuos time economic models
In this paper, I consider a general class of continuous-time economic models with unbounded horizon. I study the sets of conditions under which the policy function is continuous, Lipschitz continuous, and Cl differentiable. 1 also single out certain postulates which may prevent higher-order differentiability. The analysis provides, therefore, a fmn foundation to the use of dynamic programming methods in continuous time models with unbounded horizo
Performance Analysis for Time-of-Arrival Estimation with Oversampled Low-Complexity 1-bit A/D Conversion
Analog-to-digtial (A/D) conversion plays a crucial role when it comes to the
design of energy-efficient and fast signal processing systems. As its
complexity grows exponentially with the number of output bits, significant
savings are possible when resorting to a minimum resolution of a single bit.
However, then the nonlinear effect which is introduced by the A/D converter
results in a pronounced performance loss, in particular for the case when the
receiver is operated outside the low signal-to-noise ratio (SNR) regime. By
trading the A/D resolution for a moderately faster sampling rate, we show that
for time-of-arrival (TOA) estimation under any SNR level it is possible to
obtain a low-complexity -bit receive system which features a smaller
performance degradation then the classical low SNR hard-limiting loss of
( dB). Key to this result is the employment of a lower bound for
the Fisher information matrix which enables us to approximate the estimation
performance for coarsely quantized receivers with correlated noise models in a
pessimistic way
Consistency properties of a simulation-based estimator for dynamic processes
This paper considers a simulation-based estimator for a general class of
Markovian processes and explores some strong consistency properties of the
estimator. The estimation problem is defined over a continuum of invariant
distributions indexed by a vector of parameters. A key step in the method of
proof is to show the uniform convergence (a.s.) of a family of sample
distributions over the domain of parameters. This uniform convergence holds
under mild continuity and monotonicity conditions on the dynamic process. The
estimator is applied to an asset pricing model with technology adoption. A
challenge for this model is to generate the observed high volatility of stock
markets along with the much lower volatility of other real economic aggregates.Comment: Published in at http://dx.doi.org/10.1214/09-AAP608 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Network-based ranking in social systems: three challenges
Ranking algorithms are pervasive in our increasingly digitized societies,
with important real-world applications including recommender systems, search
engines, and influencer marketing practices. From a network science
perspective, network-based ranking algorithms solve fundamental problems
related to the identification of vital nodes for the stability and dynamics of
a complex system. Despite the ubiquitous and successful applications of these
algorithms, we argue that our understanding of their performance and their
applications to real-world problems face three fundamental challenges: (i)
Rankings might be biased by various factors; (2) their effectiveness might be
limited to specific problems; and (3) agents' decisions driven by rankings
might result in potentially vicious feedback mechanisms and unhealthy systemic
consequences. Methods rooted in network science and agent-based modeling can
help us to understand and overcome these challenges.Comment: Perspective article. 9 pages, 3 figure
Rational asset pricing bubbles
This paper provides a fairly systematic study of general economic conditions under which rational asset pricing bubbles may arise in an intertemporal competitive equilibrium framework. Our main results are concerned with non-existence of asset pricing bubbles in those economies. These results imply that the conditions under which bubbles are possible inc1uding sorne well-known examples of monetary equilibria-are relatively fragile
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