9,173 research outputs found
Non-Data-Aided Parameter Estimation in an Additive White Gaussian Noise Channel
Non-data-aided (NDA) parameter estimation is considered for
binary-phase-shift-keying transmission in an additive white Gaussian noise
channel. Cramer-Rao lower bounds (CRLBs) for signal amplitude, noise variance,
channel reliability constant and bit-error rate are derived and it is shown how
these parameters relate to the signal-to-noise ratio (SNR). An alternative
derivation of the iterative maximum likelihood (ML) SNR estimator is presented
together with a novel, low complexity NDA SNR estimator. The performance of the
proposed estimator is compared to previously suggested estimators and the CRLB.
The results show that the proposed estimator performs close to the iterative ML
estimator at significantly lower computational complexity
Visualization on colour based flow vector of thermal image for movement detection during interactive session
Recently thermal imaging is exploited in applications such as motion and face detection. It has drawn attention many researchers to build such technology to improve lifestyle. This work proposed a technique to detect and identify a motion in sequence images for the application in security monitoring system or outdoor surveillance. Conventional system might cause false information with the present of shadow. Thus, methods employed in this work are Canny edge detector method, Lucas Kanade and Horn Shunck algorithms, to overcome the major problem when using thresholding method, which is only intensity or pixel magnitude is considered instead of relationships between the pixels. The results obtained could be observed in flow vector parameter and the segmentation colour based image for the time frame from 1 to 10 seconds. The visualization of both the parameters clarified the movement and changes of pixel intensity between two frames by the supportive colour segmentation, either in smooth or rough motion. Thus, this technique may contribute to others application such as biometrics, military system, and surveillance machine
ML Detection in Phase Noise Impaired SIMO Channels with Uplink Training
The problem of maximum likelihood (ML) detection in training-assisted
single-input multiple-output (SIMO) systems with phase noise impairments is
studied for two different scenarios, i.e. the case when the channel is
deterministic and known (constant channel) and the case when the channel is
stochastic and unknown (fading channel). Further, two different operations with
respect to the phase noise sources are considered, namely, the case of
identical phase noise sources and the case of independent phase noise sources
over the antennas. In all scenarios the optimal detector is derived for a very
general parametrization of the phase noise distribution. Further, a high
signal-to-noise-ratio (SNR) analysis is performed to show that
symbol-error-rate (SER) floors appear in all cases. The SER floor in the case
of identical phase noise sources (for both constant and fading channels) is
independent of the number of antenna elements. In contrast, the SER floor in
the case of independent phase noise sources is reduced when increasing the
number of antenna elements (for both constant and fading channels). Finally,
the system model is extended to multiple data channel uses and it is shown that
the conclusions are valid for these setups, as well.Comment: (To appear in IEEE Transactions on Communications, 2015), Contains
additional material (Appendix B. T-slot Detectors
Nonlinear Channel Estimation for OFDM System by Complex LS-SVM under High Mobility Conditions
A nonlinear channel estimator using complex Least Square Support Vector
Machines (LS-SVM) is proposed for pilot-aided OFDM system and applied to Long
Term Evolution (LTE) downlink under high mobility conditions. The estimation
algorithm makes use of the reference signals to estimate the total frequency
response of the highly selective multipath channel in the presence of
non-Gaussian impulse noise interfering with pilot signals. Thus, the algorithm
maps trained data into a high dimensional feature space and uses the structural
risk minimization (SRM) principle to carry out the regression estimation for
the frequency response function of the highly selective channel. The
simulations show the effectiveness of the proposed method which has good
performance and high precision to track the variations of the fading channels
compared to the conventional LS method and it is robust at high speed mobility.Comment: 11 page
Frequency estimation in multipath rayleigh-sparse-fading channels
Maximum-likelihood (ML) data-aided frequency estimation in multipath Rayleigh-fading channels with sparse impulse responses is investigated. We solve this problem under the assumption that the autocorrelation matrix of the pilot signal can be approximated by a diagonal matrix, the fading of different path amplitudes are independent from each other, and the additive noise is white and Gaussian. The ML frequency estimator is shown to be based on combining nonlinearly transformed path periodograms. We have derived the nonlinear function for the two cases: known and unknown fading variances. The new frequency estimators lead, in particular cases, to known ML frequency estimators for nonsparse multipath fading channels. The use of a priori information about the mean number of paths in the channel allows a significant improvement of the accuracy performance. Exploiting the sparseness of the channel impulse response is shown to significantly reduce the threshold signal-to-noise ratio at which the frequency error departs from the Cramer-Rao lower bound. However, precise knowledge of the channel sparseness is not required in order to realize this improvement
Multiple Parameter Estimation With Quantized Channel Output
We present a general problem formulation for optimal parameter estimation
based on quantized observations, with application to antenna array
communication and processing (channel estimation, time-of-arrival (TOA) and
direction-of-arrival (DOA) estimation). The work is of interest in the case
when low resolution A/D-converters (ADCs) have to be used to enable higher
sampling rate and to simplify the hardware. An Expectation-Maximization (EM)
based algorithm is proposed for solving this problem in a general setting.
Besides, we derive the Cramer-Rao Bound (CRB) and discuss the effects of
quantization and the optimal choice of the ADC characteristic. Numerical and
analytical analysis reveals that reliable estimation may still be possible even
when the quantization is very coarse.Comment: 9 pages, 9 figures, International ITG Workshop on Smart Antennas -
WSA 2010, Bremen, German
Power delay profile and noise variance estimation for OFDM
In this letter, we present cyclic-prefix (CP) based noise-variance and power-delay-profile estimators for Orthogonal Frequency Division Multiplexing (OFDM) systems. Signal correlation due to the use of the CP is exploited without requiring additional pilot symbols. A heuristic estimator and a class of approximate maximum likelihood (ML) estimators are proposed. The proposed algorithms can be applied to both unitary and non-unitary constellations. These algorithms can be readily used for applications such as minimum mean-square error (MMSE) channel estimation
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