33,738 research outputs found
Cramer-Rao bounds in the estimation of time of arrival in fading channels
This paper computes the Cramer-Rao bounds for the time of arrival estimation in a multipath Rice and Rayleigh fading scenario, conditioned to the previous estimation of a set of propagation channels, since these channel estimates (correlation between received signal and the pilot sequence) are sufficient statistics in the estimation of delays. Furthermore, channel estimation is a constitutive block in receivers, so we can take advantage of this information to improve timing estimation by using time and space diversity. The received signal is modeled as coming from a scattering environment that disperses the signal both in space and time. Spatial scattering is modeled with a Gaussian distribution and temporal dispersion as an exponential random variable. The impact of the sampling rate, the roll-off factor, the spatial and temporal correlation among channel estimates, the number of channel estimates, and the use of multiple sensors in the antenna at the receiver is studied and related to the mobile subscriber positioning issue. To our knowledge, this model is the only one of its kind as a result of the relationship between the space-time diversity and the accuracy of the timing estimation.Peer ReviewedPostprint (published version
Determining the number of breaks in a piecewise linear regression model
In this paper we propose a sequential method for determining the number of breaks in piecewise linear structural break models. An advantage of the method is that it is based on standard statistical inference. Tests available for testing linearity against switching regression type nonlinearity are applied sequentially to determine the number of regimes in the structural break model. A simulation study is performed in order to investigate the finite-sample behaviour of the procedure and to compare it with other alternatives. We find that our method works well in comparison for both single and multiple break cases.Model specification; multiple structural breaks.
Parameter Estimation from Time-Series Data with Correlated Errors: A Wavelet-Based Method and its Application to Transit Light Curves
We consider the problem of fitting a parametric model to time-series data
that are afflicted by correlated noise. The noise is represented by a sum of
two stationary Gaussian processes: one that is uncorrelated in time, and
another that has a power spectral density varying as . We present
an accurate and fast [O(N)] algorithm for parameter estimation based on
computing the likelihood in a wavelet basis. The method is illustrated and
tested using simulated time-series photometry of exoplanetary transits, with
particular attention to estimating the midtransit time. We compare our method
to two other methods that have been used in the literature, the time-averaging
method and the residual-permutation method. For noise processes that obey our
assumptions, the algorithm presented here gives more accurate results for
midtransit times and truer estimates of their uncertainties.Comment: Accepted in ApJ. Illustrative code may be found at
http://www.mit.edu/~carterja/code/ . 17 page
Random Access in Massive MIMO by Exploiting Timing Offsets and Excess Antennas
Massive MIMO systems, where base stations are equipped with hundreds of
antennas, are an attractive way to handle the rapid growth of data traffic. As
the number of user equipments (UEs) increases, the initial access and handover
in contemporary networks will be flooded by user collisions. In this paper, a
random access protocol is proposed that resolves collisions and performs timing
estimation by simply utilizing the large number of antennas envisioned in
Massive MIMO networks. UEs entering the network perform spreading in both time
and frequency domains, and their timing offsets are estimated at the base
station in closed-form using a subspace decomposition approach. This
information is used to compute channel estimates that are subsequently employed
by the base station to communicate with the detected UEs. The favorable
propagation conditions of Massive MIMO suppress interference among UEs whereas
the inherent timing misalignments improve the detection capabilities of the
protocol. Numerical results are used to validate the performance of the
proposed procedure in cellular networks under uncorrelated and correlated
fading channels. With UEs that may simultaneously become active
with probability 1\% and a total of frequency-time codes (in a given
random access block), it turns out that, with antennas, the proposed
procedure successfully detects a given UE with probability 75\% while providing
reliable timing estimates.Comment: 30 pages, 6 figures, 1 table, submitted to Transactions on
Communication
A kepstrum approach to filtering, smoothing and prediction
The kepstrum (or complex cepstrum) method is revisited and applied to the problem of spectral factorization
where the spectrum is directly estimated from observations. The solution to this problem in turn leads to a new
approach to optimal filtering, smoothing and prediction using the Wiener theory. Unlike previous approaches to
adaptive and self-tuning filtering, the technique, when implemented, does not require a priori information on the
type or order of the signal generating model. And unlike other approaches - with the exception of spectral
subtraction - no state-space or polynomial model is necessary. In this first paper results are restricted to
stationary signal and additive white noise
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