@PHDTHESIS\{IMM2007-04933,
    author       = "L. P. B. Christensen",
    title        = "Signal Processing for Improved Wireless Receiver Performance",
    year         = "2007",
    month        = "jun",
    keywords     = "Wireless Communications, Physical Layer Signal Processing, Approximate Inference, Generalized Belief Propagation, Parameter Estimation, {VBEM-}algorithm",
    school       = "Informatics and Mathematical Modelling, Technical University of Denmark, {DTU}",
    address      = "Richard Petersens Plads, Building 321, {DK-}2800 Kgs. Lyngby",
    type         = "",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/4933-full.html",
    abstract     = "This thesis is concerned with signal processing for improving the
performance of wireless communication receivers for well-established
cellular networks such as the {GSM}/{EDGE} and {WCDMA}/{HSPA} systems. The
goal of doing so, is to improve the end-user experience and/or
provide a higher system capacity by allowing an increased reuse of
network resources.

To achieve this goal, one must first understand the nature of the
problem and an introduction is therefore provided. In addition, the
concept of graph-based models and approximations for wireless
communications is introduced along with various Belief Propagation
(BP) methods for detecting the transmitted information, including
the Turbo principle.

Having established a framework for the research, various approximate
detection schemes are discussed. First, the general form of linear
detection is presented and it is argued that this may be preferable
in connection with parameter estimation. Next, a realistic framework
for interference whitening is presented, allowing flexibility in the
selection of whether interference is accounted for via a discrete or
a Gaussian distribution. The approximate method of sphere detection
and decoding is outlined and various suggestions for improvements
are presented. In addition, methods for using generalized {BP} to
perform approximate joint detection and decoding in systems with
convolutional codes are outlined. One such method is a natural
generalization of the traditional Turbo principle and a generalized
Turbo principle can therefore be established.

For realistic wireless communication scenarios, a multitude of
parameters are not known and must instead be estimated. A general
variational Bayesian {EM-}algorithm is therefore presented to provide
such estimates. It generalizes previously known methods for
communication systems by estimating parameter densities instead of
point-estimates and can therefore account for uncertainty in the
parameter estimates. Finally, an {EM-}algorithm for band-Toeplitz
covariance estimation is presented as such an estimate is desirable
for noise and interference whitening. Using simulations, the method
is shown to be near-optimal in the sense that it achieves the
unbiased Cramer-Rao lower-bound for medium and large sample-sizes."
}