Added paragraph to discussion
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@ -70,6 +70,11 @@
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long = maximum likelihood
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}
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\DeclareAcronym{MIMO} {
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short = MIMO,
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long = multiple-input multiple-output
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}
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%
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% I
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%
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@ -1,12 +1,15 @@
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\chapter{Discussion}%
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\label{chapter:discussion}
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While the modified proximal decoding algorithm presented in section
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\ref{sec:prox:Improved Implementation} shows some promising results, further
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investigation is required to determine how different choices of parameters
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affect the decoding performance.
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Additionally, a more mathematically rigorous foundation for determining the
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potentially wrong components of the estimate is desirable.
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A modification of the implementation to reduce the memory requirements, even
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at some cost with regard to the running time, would allow for the examination
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of longer codes.
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This in turn would make possible studying the behavior of the decoding
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algorithms covered here in error-rate regions where traditional approaches
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exhibit an error floor.
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The decoding algorithms could then be assessed for use in very
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high reliability applications, where traditional methods like \ac{BP} or the
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min-sum-algorithm fall short.
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As mentioned in section \ref{subsec:prox:conv_properties}, the alternating
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minimization of the two gradients in the proximal decoding algorithm leads to
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@ -23,6 +26,18 @@ constraints are never truly satisfied; not even after the minimization step
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dealing with the constraint part of the objective function.
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Despite this, an initial examination by Yanxia Lu in
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\cite[Sec. 4.2.4.]{yanxia_lu_thesis} shows only limited success.
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It is also important to note that while in this thesis proximal decoding was
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examined with respect to its performance in \ac{AWGN} channels, in
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\cite{proximal_paper} it is presented as a method applicable to non-trivial
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channel models such as \ac{LDPC}-coded massive \ac{MIMO} channels, perhaps
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broadening its usefulness beyond what is shown here.
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While the modified proximal decoding algorithm presented in section
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\ref{sec:prox:Improved Implementation} shows some promising results, further
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investigation is required to determine how different choices of parameters
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affect the decoding performance.
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Additionally, a more mathematically rigorous foundation for determining the
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potentially wrong components of the estimate is desirable.
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Another interesting approach might be the combination of proximal and \ac{LP}
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decoding.
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