下一代移动无线通信系统的目标是实现无所不在的、高质量的、高速率的移动多媒体传输.htm

研究和开发移动道中数字传输技术的第一步工作就是认识移动信道本身的特性

symbol timing error is outside of the ISI free range in the guard interval, the
inaccurate symbol timing can cause ISI that destroys the orthogonality of the
sub-carriers and degrades the performance of OFDM systems. In addition, the
performance of the channel estimation via interpolation, commonly used for
coherent OFDM systems, can be essentially degraded by the symbol timing errors.
Hence, more accurate symbol timing synchronization methods are needed to
fulfill the synchronization requirement in coherent OFDM systems. In contrast
to the symbol synchronization case, the purpose of sampling clock
synchronization is to align the receiver sampling clock frequency to that of
the transmitter. The sampling clock frequency error can cause ICI. Moreover,
the sampling clock frequency error can result in a drift in the symbol timing
and can further worsen the symbol synchronization problems. Thus, sampling
clock synchronization is also an important issue that needs to be addressed in
OFDM systems. In current methods, symbol synchronization and sampling clock
synchronization are dealt with separately. In this Chapter, we propose a timing
recovery scheme based on pilot sub-carriers for OFDM systems, which can solve
the symbol synchronization and sampling clock synchronization issues
simultaneously. As pilot sub-carriers are used in most coherent OFDM systems
for synchronization and channel estimation purposes, our scheme can be implemented
without additional overhead for these systems. In this scheme, we use the
correlation method based on the guard interval to do the coarse symbol
synchronization. A path time delay estimation method is then employed to
further improve the accuracy of the coarse symbol synchronization. Finally we
use a delay-locked loop (DLL) to do the sampling clock synchronization and to
maintain the symbol timing. Other feedback loop MLL is also proposed. We derive
the both techniques from the joint maximum-likelihood (ML) estimation of the
symbol timing and carrier phase in the AWGN channel. We apply the derived
algorithm to both AWGN and various multipath fading channels and study their
performance via simulation. Analysis shows that the tracking error of MLL approaches
to the CBD bound. Even though the DLL is not optimal for the multipath fading
channels, by employing this scheme, the mean square values of the symbol timing
estimation error can be reduced by several orders of magnitude compared with
the traditional correlation methods. In addition, the proposed scheme is
capable of tracking the drift in the symbol timing caused by the sampling clock
frequency offsets.</span></p>

<p class=MsoBodyText style='text-align:justify;text-justify:inter-ideograph;
text-indent:34.5pt;mso-char-indent-count:3.0;mso-char-indent-size:11.5pt'><span
lang=EN-US style='letter-spacing:.5pt'>In Chapter 5, we present a novel channel
estimation algorithm for OFDM mobile communication systems using pilot
sub-carriers. This algorithm is based on a parametric channel model. In OFDM
systems, the channel estimation can be achieved by exploiting the correlation
of the channel frequency response at different frequencies and times. The
channel estimators for OFDM systems have been proposed based on frequency
domain filtering and time domain filtering. These methods do not make any
assumptions about the channel model and hence the dimension of the estimation
problem can be quite large. However, the radio channel in a wireless
communication system is often characterized by the multipath propagation. In
large cells with high base station antenna platforms, the multipath propagation
is aptly modeled by a few dominant specular paths, typically two to six. Moreover,
the high-speed data transmission in wireless communications potentially results
in a sparse multipath fading channel. The sparsity of a multipath channel can
be defined as the ratio of the time duration (in OFDM samples) spanned by the
multipaths to the number of the multipaths. A parametric channel model can then
be used to represent this type of channels. When the channel correlation matrix
is constructed based on the parametric channel model, the signal subspace
dimension of the correlation matrix can be effectively reduced. Accordingly,
the channel estimator performance can be improved. The parametric channel model
approach has been applied to the Global System for Mobile Communications (GSM)
system and the high-speed digital video broadcast system to improve the channel
equalizer and estimator performance. It should be also noted that in mobile
communications the multipath time delays are slowly time-varying. In contrast,
the amplitude and relative phase of each path are relatively fast time-varying
and subject to (Rayleigh) fading. We can thus take this into account in
designing the channel estimator. In this Chapter, we propose an improved
channel estimation method for OFDM transmission over the sparse multipath
fading channels using pilot sub-carriers. The channel estimator is based on a
parametric channel model. That is, the channel frequency response of the
multipath fading channel is modeled as the Fourier transform of a multipath
finite impulse response. The channel estimator is derived to estimate the
parameters which include the time delays, gains, and phases of the paths.
Specifically, we first use the minimum description length (MDL) criterion to
detect the number of paths in the channel. Then, we use the Estimation of
Signal Parameters by Rotational Invariance Techniques (ESPRIT) to estimate the
initial multipath time delays. Because of the slow time-varying nature of time
delays, we propose an inter-path interference cancellation (IPIC) delay locked
loop (DLL) to track the channel multipath time delays. With the multipath time
delays information, a MMSE estimator is derived to estimate the channel
frequency response. The simulation results show that the MDL criterion and the
ESPRIT method can adaptively estimate the initial channel parameters. Further,
the IPIC DLL is shown to be an effective way to estimate and track the
multipath time delays. Analysis and simulation results also demonstrate that
the proposed channel estimation algorithm gives a substantial performance
improvement in MSE over the non-parametric channel model based methods for the
sparse multipath fading channels.</span><span lang=EN-US style='mso-fareast-font-family:
宋体;letter-spacing:.5pt;mso-fareast-language:ZH-CN'><o:p></o:p></span></p>

<p class=MsoNormal style='text-indent:34.5pt;mso-char-indent-count:3.0;
mso-char-indent-size:11.5pt;mso-pagination:widow-orphan;mso-layout-grid-align:
none;text-autospace:none'><span lang=EN-US style='letter-spacing:.5pt'>In
Chapter 6, low-complexity windowed discrete Fourier transform (DFT) based MMSE channel
estimators are proposed and analyzed for both the interpolation and non-interpolation
cases for OFDM mobile communications systems. </span><span lang=EN-US
style='mso-fareast-font-family:"Times New Roman";letter-spacing:.5pt;
mso-font-kerning:0pt;mso-fareast-language:EN-US'>The Minimum Mean Square Error
(MMSE) interpolator and filter have been proposed to do channel estimation for
OFDM systems. However, the complexity of the optimal MMSE estimator is usually
high. The discrete Fourier transform (DFT) based channel estimator gives us an
alternative and low-complexity choice. The DFT based method can be used for
both the interpolation case and the non-interpolation case. For the
interpolation case, the DFT is a simple and computationally efficient approach
for performing interpolation. However, the perfect interpolation of a N-sample
complex sequence by DFT not only requires that the sequence represents N
equispaced samples of a continuous signal that is band-limited below the
Nyquist limit, but also that the signal has a discrete spectral density
distribution. In the application of the channel estimation for the OFDM
systems, the above discrete spectral density requirement means that the channel
multipath time delays must be sample-spaced. In practice, while the channel
impulse response has a finite duration that is below the Nyquist sampling rate
limit in the frequency domain, the channel multipath time delays will, in
general, be non-sample-spaced or the channel will, in general, have a
continuous rather than discrete power delay profile. In this case, if no data
windowing is applied to the channel frequency response observation vector
before the interpolation by DFT, the aliased spectral leakage can cause an
error floor. In addition, since the channel frequency response is usually
oversampled by the pilot sub-carriers, the channel power will be concentrated
to a relatively small number of samples in the effective channel impulse
response vector. On the other hand, the noise power is spread over the vector.
Hence, a weighting function can be applied to the vector to suppress the
channel noise. For the non-interpolation case, the DFT based estimators also
employ the low-complexity property of DFT and the channel power concentration
property of the effective channel impulse response. In this Chapter, we present
the low-complexity windowed DFT based MMSE channel estimators for both the
interpolation and non-interpolation cases. A generalized Hanning window is
first applied to the channel frequency response observation vector to reduce
the spectral leakage. Moreover, a weighting function is applied to the
effective channel impulse response. The weighting function is chosen so that
the MSE between the channel frequency response and its estimate is minimized
for a given window function. Since the resulting MSE also depends on the window
shape, the optimal generalized Hanning window shape is also searched to
minimize the MSE. Analysis and simulation results show that the data windowing
can eliminate the error floor for the interpolation case, and can bring about
better noise filtering performance for the non-interpolation case. Moreover,
the MMSE weighting is an effective technique to suppress the channel noise and
improve the channel estimation performance. It is shown that the optimal MMSE
weighting functions always exist for both cases. It has been also shown that
the proposed method performance is close to the conventional optimal MMSE
channel estimator and is much better than the direct DFT based estimator.
Furthermore, it has much lower complexity than the optimal MMSE estimator
because the IDFT/DFT transforms can be implemented with the fast algorithms
IFFT/FFT and the MMSE weighting operation in the time domain is a simple element-by-element
multiplication.</span><span lang=EN-US style='letter-spacing:.5pt'><o:p></o:p></span></p>

<p class=MsoBodyTextIndent style='text-indent:34.5pt'><span lang=EN-US
style='letter-spacing:.5pt'>Finally, Chapter 7 summarizes the dissertation and
gives some suggestions about future research directions about OFDM.<o:p></o:p></span></p>

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.5pt;mso-fareast-language:ZH-CN'><![if !supportEmptyParas]>&nbsp;<![endif]><o:p></o:p></span></p>

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