With the increasing demand for more wireless multimedia applications,
it is desired to design a wireless system with higher data rate.
Furthermore, the frequency spectrum has become
a limited and valuable resource, making
it necessary to utilize the available spectrum efficiently
and coexist with other wireless systems.
Orthogonal frequency division multiplexing (OFDM)
modulation is widely used in communication systems to meet the
demand for ever increasing data rates. The major advantage of OFDM
over single-carrier transmission is its ability to deal with severe
channel conditions without complex equalization. However, OFDM systems suffer from
a high peak to average power ratio, and they are sensitive to carrier frequency offset and Doppler spread.
This dissertation first focuses on the development of intercarrier interference (ICI) reduction and signal detection
algorithms for OFDM systems over time-varying channels.
Several ICI reduction algorithms are proposed for OFDM systems over
doubly-selective channels. The OFDM ICI reduction problem over time-varying channels
is formulated as a combinatorial optimization problem based on the maximum likelihood (ML)
criterion. First, two
relaxation methods are utilized to convert the ICI reduction problem
into convex quadratic programming (QP) problems. Next, a low
complexity ICI reduction algorithm applicable to $M$-QAM signal
constellations for OFDM systems is proposed.
This formulates the ICI reduction problem as a QP problem with non-convex constraints. A
successive method is then utilized to deduce a sequence of
reduced-size QP problems. For the proposed algorithms, the QP
problems are solved by limiting the search in the 2-dimensional
subspace spanned by its steepest-descent and Newton directions to
reduce the computational complexity. Furthermore, a low-bit descent
search (LBDS) is employed to improve the system performance.
Performance results are given to demonstrate that the proposed
ICI reduction algorithms provide excellent performance with
reasonable computational complexity.
A low complexity joint semiblind
detection algorithm based on the channel correlation and noise
variance is proposed which does not require channel state information.
The detection problem is relaxed
to a continuous non-convex quadratic programming problem. Then an
iterative method is utilized to deduce a sequence of reduced-size
quadratic programming problems.
A LBDS method is also employed
to improve the solution of the derived QP problems. Results are
given which demonstrate that the proposed algorithm provides
similar performance with lower computational complexity compared to
that of a sphere decoder.
A major challenge to OFDM systems is how to obtain accurate
channel state information for coherent detection of the transmitted signals. Thus
several channel estimation algorithms are proposed for OFDM systems
over time-invariant channels.
A channel estimation method is developed to utilize
the noncircularity of the input signals to
obtain an estimate of the channel coefficients.
It takes advantage of the nonzero cyclostationary
statistics of the transmitted signals,
which in turn allows blind polynomial channel estimation using
second-order statistics of the OFDM symbol.
A set of polynomial equations are formulated based on the correlation
of the received signal which can be used to obtain an
estimate of the time domain channel coefficients.
Performance results are presented which show that the proposed algorithm
provides better performance than the least minimum mean-square error (LMMSE)
algorithm at high signal to noise ratios (SNRs), with low
computational complexity.
Near-optimal performance can be achieved with large OFDM systems.
Finally, a CS-based time-domain channel estimation method is presented
for OFDM systems over sparse channels. The channel estimation
problem under consideration is formulated as a small-scale
$l_1$-minimization problem which is convex and admits fast and
reliable solvers for the globally optimal solution. It is
demonstrated that the magnitudes as well as delays of the
significant taps of a sparse channel model can be estimated with
satisfactory accuracy by using fewer pilot tones than the channel
length. Moreover, it is shown that a fast Fourier transform (FFT)
matrix of extended size can be used as a set of appropriate basis
vectors to enhance the channel sparsity. This
technique allows the proposed method to be applicable to
less-sparse OFDM channels. In addition, a total-variation (TV)
minimization based method is introduced to provide an alternative
way to solve the original sparse channel estimation problem. The
performance of the proposed method is compared to several
established channel estimation algorithms. / Graduate
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/3462 |
| Date | 16 August 2011 |
| Creators | Zhang, Yihai |
| Contributors | Lu, Wu-Sheng, Gulliver, T. Aaron |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | Available to the World Wide Web |
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