A system simulation environment in Matlab/Simulink of RFID is constructed in this paper.
Special attention is emphasized on the analog/RF circuit.Negative effects are concerned in the system
model,such as phase noise of the local oscillator,TX-RX coupling,reflection of the environment,
AWGN noise,DC offset,I/Q mismatch,etc.Performance of the whole system can be evaluated by
changing the coding method,parameters of building blocks,and operation distance.Finally,some
simulation results are presented in this paper.
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
This paper examines the asymptotic (large sample) performance
of a family of non-data aided feedforward (NDA FF) nonlinear
least-squares (NLS) type carrier frequency estimators for burst-mode
phase shift keying (PSK) modulations transmitted through AWGN and
flat Ricean-fading channels. The asymptotic performance of these estimators
is established in closed-form expression and compared with the
modified Cram`er-Rao bound (MCRB). A best linear unbiased estimator
(BLUE), which exhibits the lowest asymptotic variance within the family
of NDA FF NLS-type estimators, is also proposed.
In recent years large scientific interest has been
devoted to joint data decoding and parameter estimation
techniques. In this paper, iterative turbo decoding joint
to channel frequency and phase estimation is proposed.
The phase and frequency estimator is embedded into the
structure of the turbo decoder itself, taking into consideration
both turbo interleaving and puncturing. Results
show that the proposed technique outperforms conventional
approaches both in terms of detection capabilities and
implementation complexity.
A new cable fault location method based on
wavelet reconstruction is proposed. In this method the
difference between the currents of faulty phase and sound
phase under the high voltage pulse excitation is used as the
measured signal and is decomposed in multi-scale by wavelet
transform, then reconstructed in single scale. Comparing with
traditional fault location method by travelling wave, the
presented method will not be interfered by the reflected wave
from the branch joint of cables or from other positions where
the impedances are not matched and not be influenced by fault
types, otherwise, the reflected waves can be recognized even
the faulty position is near to the measuring terminal, at the
same time, the influence of the wave speed uncertainty can be
reduced. The correctness of the proposed method is proved by
simulation results.
A new PLL topology and a new simplified linear model are presented. The new fractional-N synthesizer presents no reference spurs and lowers the overall phase noise, thanks to the presence of a SampleJHold block. With a new simulation methodology it is possible to perform very accurate simulations, whose results match closely those obtained with the linear PLL model developed.
Fast settling-time added to the already conflicting requirements of narrow channel spacing and
low phase noise lead to Fractional4 divider techniques for PLL synthesizers. We analyze discrete "beat-note spurious levels from arbitrary modulus divide sequences including those from classic accumulator methods.
％％％ Demos for PUMA algorithms ％％％
We present four matlab demos for PUMA. demo1, demo2, demo3, and demo4
illustrate PUMA working with different parameters and with four
different images.
All you need to do is to run each of the demos. Please be sure that
all the files are put on an accessible path for matlab.
Notice that this code is intended for research purposes only.
For further reference see "Phase Unwrapping via Graph Cuts,
IEEE Transactions on Image Processing, 2007
