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@PHDTHESIS{Senst:685621,
author = {Senst, Martin},
othercontributors = {Ascheid, Gerd and Vary, Peter},
title = {{O}n the design of iterative wireless receivers : the
divergence minimization approach to approximate {B}ayesian
inference},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2017-02399},
pages = {1 Online-Ressource (xii, 275 Seiten) : Diagramme},
year = {2016},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2017; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2016},
abstract = {The discovery of turbo codes in the 1990s has
revolutionized the design of communication systems. Unlike
traditional channel codes, they are characterized by a
complex, pseudo-random structure which enables datarates
close to the channel capacity. The key innovation of turbo
codes has been their novel approach to decoding: it consists
of two constituent decoders which run alternately several
times and exchange information about the transmitted data
during this process. It has been observed empirically that
this iterative scheme converges with a high probability to
the correct solution, despite the lack of any theoretical
guarantees. Following the remarkable success of turbo codes,
the concept of iterative information processing has also
been applied to other tasks of wireless receivers, yielding
techniques like code-aided synchronization and iterative
detection and decoding.Initially, the proposed algorithms
were rather heuristic, but a significant research effort has
been invested into the development of a theoretical
foundation. An important step has been the derivation of the
turbo decoder as an instance of belief propagation (BP), a
framework for solving Bayesian inference problems.
Unfortunately, it turns out that BP is less suitable for
other tasks beyond the channel decoder. For example, using
BP for the design of code-aided synchronization schemes,
which require the estimation of continuous variables, gives
rise to integrals that typically do not admit a closed-form
solution. For this class of problems, the
expectation-maximization (EM) algorithm has emerged as a
better alternative, which however has its own drawbacks.
Additionally, high-dimensional detection problems, which
arise for example in the context of multi-antenna (MIMO)
systems, involve sums with exponentially many terms whose
evaluation is practically infeasible.In this thesis, we
investigate the design of iterative wireless receivers based
on a generic framework for approximate Bayesian inference
that has recently been developed in the machine learning
community. Its key idea is the conversion of the original
summation or integration problem into an equivalent
optimization problem, which is then solved approximately by
an iterative algorithm. As it consists of the minimization
of a divergence measure between the probabilistic system
model and a simpler auxiliary distribution, we refer to this
approach as divergence minimization (DM). Due to its
generality, DM provides the designer with a great deal of
flexibility, and it contains specific algorithms like BP and
EM as special cases.This thesis begins with a systematic
derivation of a combined EM- and BP-based receiver, which
has been proposed earlier in the literature in a rather
ad-hoc way. We then utilize the flexibility of DM for the
development of several novel parameter estimation and MIMO
detection algorithms, which due to their good performance
and low computational complexity are interesting options for
practical implementations. Further, this thesis also
contributes to the theoretical understanding of iterative
methods. In earlier work, the subcomponents were often
designed separately and then connected heuristically. In
contrast, the virtue of the proposed holistic approach to
receiver design is that it does not only specify the
individual functional units but also their interactions. In
particular, there has been some confusion in the literature
on whether the components should exchange extrinsic
information as in the turbo decoder, or whether exchanging
the full posterior information is preferable. The
derivations that are presented in this thesis shed light on
this important question.},
cin = {611810},
ddc = {621.3},
cid = {$I:(DE-82)611810_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2017-02399},
url = {https://publications.rwth-aachen.de/record/685621},
}
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