29,245 research outputs found
Mean Estimation from Adaptive One-bit Measurements
We consider the problem of estimating the mean of a normal distribution under
the following constraint: the estimator can access only a single bit from each
sample from this distribution. We study the squared error risk in this
estimation as a function of the number of samples and one-bit measurements .
We consider an adaptive estimation setting where the single-bit sent at step
is a function of both the new sample and the previous acquired bits.
For this setting, we show that no estimator can attain asymptotic mean squared
error smaller than times the variance. In other words,
one-bit restriction increases the number of samples required for a prescribed
accuracy of estimation by a factor of at least compared to the
unrestricted case. In addition, we provide an explicit estimator that attains
this asymptotic error, showing that, rather surprisingly, only times
more samples are required in order to attain estimation performance equivalent
to the unrestricted case
Adaptive Differential Feedback in Time-Varying Multiuser MIMO Channels
In the context of a time-varying multiuser multiple-input-multiple-output
(MIMO) system, we design recursive least squares based adaptive predictors and
differential quantizers to minimize the sum mean squared error of the overall
system. Using the fact that the scalar entries of the left singular matrix of a
Gaussian MIMO channel becomes almost Gaussian distributed even for a small
number of transmit antennas, we perform adaptive differential quantization of
the relevant singular matrix entries. Compared to the algorithms in the
existing differential feedback literature, our proposed quantizer provides
three advantages: first, the controller parameters are flexible enough to adapt
themselves to different vehicle speeds; second, the model is backward adaptive
i.e., the base station and receiver can agree upon the predictor and variance
estimator coefficients without explicit exchange of the parameters; third, it
can accurately model the system even when the correlation between two
successive channel samples becomes as low as 0.05. Our simulation results show
that our proposed method can reduce the required feedback by several kilobits
per second for vehicle speeds up to 20 km/h (channel tracker) and 10 km/h
(singular vector tracker). The proposed system also outperforms a fixed
quantizer, with same feedback overhead, in terms of bit error rate up to 30
km/h.Comment: IEEE 22nd International Conference on Personal, Indoor and Mobile
Radio Communications (2011
Mean Estimation from One-Bit Measurements
We consider the problem of estimating the mean of a symmetric log-concave
distribution under the constraint that only a single bit per sample from this
distribution is available to the estimator. We study the mean squared error as
a function of the sample size (and hence the number of bits). We consider three
settings: first, a centralized setting, where an encoder may release bits
given a sample of size , and for which there is no asymptotic penalty for
quantization; second, an adaptive setting in which each bit is a function of
the current observation and previously recorded bits, where we show that the
optimal relative efficiency compared to the sample mean is precisely the
efficiency of the median; lastly, we show that in a distributed setting where
each bit is only a function of a local sample, no estimator can achieve optimal
efficiency uniformly over the parameter space. We additionally complement our
results in the adaptive setting by showing that \emph{one} round of adaptivity
is sufficient to achieve optimal mean-square error
Decision Directed Channel Estimation Aided OFDM Employing Sample-Spaced and Fractionally-Spaced CIR Estimators
Abstract—In this letter we characterize the substantial difference between two channel estimation approaches, namely the sample-spaced (SS) and the fractionally-spaced (FS) channel impulse response (CIR) estimators. The achievable performance of decision-directed channel estimation (DDCE) methods employing both the SS- and the FS-CIR estimators is analyzed in the context of an OFDM system. The performance of the two estimation methods is compared and it is shown that the DDCE scheme employing the Projection Approximation Subspace Tracking (PAST)-aided FS-CIR estimator outperforms its SS-CIR estimator-based counterpart. Index Terms—Multiuser OFDM, decision directed channel estimation, impulse response estimation SDMA
On a Hybrid Preamble/Soft-Output Demapper Approach for Time Synchronization for IEEE 802.15.6 Narrowband WBAN
In this paper, we present a maximum likelihood (ML) based time
synchronization algorithm for Wireless Body Area Networks (WBAN). The proposed
technique takes advantage of soft information retrieved from the soft demapper
for the time delay estimation. This algorithm has a low complexity and is
adapted to the frame structure specified by the IEEE 802.15.6 standard for the
narrowband systems. Simulation results have shown good performance which
approach the theoretical mean square error limit bound represented by the
Cramer Rao Bound (CRB)
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