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9
latex/thesis/bibliography.bib
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@ -223,3 +223,12 @@
date = {2023-04},
url = {http://www.inference.org.uk/mackay/codes/data.html}
}
@article{adam,
title={Adam: A method for stochastic optimization},
author={Kingma, Diederik P and Ba, Jimmy},
journal={arXiv preprint arXiv:1412.6980},
year={2014},
doi={10.48550/arXiv.1412.6980}
}

54
latex/thesis/chapters/comparison.tex
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@ -3,7 +3,7 @@
In this chapter, proximal decoding and \ac{LP} Decoding using \ac{ADMM} are compared.
First, the two algorithms are studied on a theoretical basis.
Subsequently, their respective simulation results are examined and their
Subsequently, their respective simulation results are examined, and their
differences are interpreted based on their theoretical structure.
@ -32,13 +32,13 @@ $\mathcal{P}_{d_j}, \hspace{1mm} j\in\mathcal{J}$, defined as%
%
by moving the constraints into the objective function, as shown in figure
\ref{fig:ana:theo_comp_alg:admm}.
Both algorithms are composed of an iterative approach consisting of two
alternating steps.
The objective functions of the two problems are similar in that they
both comprise two parts: one associated to the likelihood that a given
codeword was sent, stemming from the channel model, and one associated
to the constraints the decoding process is subjected to, stemming from the
codeword was sent, arising from the channel model, and one associated
to the constraints the decoding process is subjected to, arising from the
code used.
Both algorithms are composed of an iterative approach consisting of two
alternating steps, each minimizing one part of the objective function.
%
\begin{figure}[h]
@ -139,7 +139,7 @@ This means that additional redundant parity-checks can be added successively
until the codeword returned is valid and thus the \ac{ML} solution is found
\cite[Sec. IV.]{alp}.
In terms of time complexity the two decoding algorithms are comparable.
In terms of time complexity, the two decoding algorithms are comparable.
Each of the operations required for proximal decoding can be performed
in $\mathcal{O}\left( n \right) $ time for \ac{LDPC} codes (see section
\ref{subsec:prox:comp_perf}).
@ -172,10 +172,10 @@ while stopping critierion unfulfilled do
|\vspace{0.22mm}\Reactivatenumber|
end for
for i in $\mathcal{I}$ do
$s_i \leftarrow s_i + \gamma \left[ 4\left( s_i^2 - 1 \right)s_i
\phantom{\frac{4}{s_i}}\right.$|\Suppressnumber|
|\Reactivatenumber|$\left.+ \frac{4}{s_i}\sum_{j\in N_v\left( i \right) }
M_{j\to i} \right] $
$s_i\leftarrow \Pi_\eta \left( s_i + \gamma \left( 4\left( s_i^2 - 1 \right)s_i
\phantom{\frac{4}{s_i}}\right.\right.$|\Suppressnumber|
|\Reactivatenumber|$\left.\left.+ \frac{4}{s_i}\sum_{j\in
N_v\left( i \right) } M_{j\to i} \right)\right) $
$r_i \leftarrow r_i + \omega \left( s_i - y_i \right)$
end for
end while
@ -232,7 +232,7 @@ With proximal decoding this minimization is performed for all constraints at onc
in an approximative manner, while with \ac{LP} decoding using \ac{ADMM} it is
performed for each constraint individually and with exact results.
In terms of time complexity, both algorithms are linear with
respect to $n$ and are heavily parallelisable.
respect to $n$ and are heavily parallelizable.
@ -241,18 +241,18 @@ respect to $n$ and are heavily parallelisable.
\label{sec:comp:res}
The decoding performance of the two algorithms is compared in figure
\ref{fig:comp:prox_admm_dec} in the form of the \ac{FER}.
\ref{fig:comp:prox_admm_dec} in form of the \ac{FER}.
Shown as well is the performance of the improved proximal decoding
algorithm presented in section \ref{sec:prox:Improved Implementation}.
The \ac{FER} resulting from decoding using \ac{BP} and,
wherever available, the \ac{FER} of \ac{ML} decoding taken from
\cite{lautern_channelcodes} are plotted as a reference.
wherever available, the \ac{FER} of \ac{ML} decoding, taken from
\cite{lautern_channelcodes}, are plotted as a reference.
The parameters chosen for the proximal and improved proximal decoders are
$\gamma=0.05$, $\omega=0.05$, $K=200$, $\eta = 1.5$ and $N=12$.
The parameters chosen for \ac{LP} decoding using \ac{ADMM} are $\mu = 5$,
$\rho = 1$, $K=200$, $\epsilon_\text{pri} = 10^{-5}$ and
$\epsilon_\text{dual} = 10^{-5}$.
For all codes considered in the scope of this work, \ac{LP} decoding using
For all codes considered within the scope of this work, \ac{LP} decoding using
\ac{ADMM} consistently outperforms both proximal decoding and the improved
version, reaching very similar performance to \ac{BP}.
The decoding gain heavily depends on the code, evidently becoming greater for
@ -268,8 +268,12 @@ calculations performed in each case.
With proximal decoding, the calculations are approximate, leading
to the constraints never being quite satisfied.
With \ac{LP} decoding using \ac{ADMM},
the constraints are fulfilled for each parity check individualy after each
the constraints are fulfilled for each parity check individually after each
iteration of the decoding process.
A further contributing factor might be the structure of the optimization
process, as the alternating minimization with respect to the same variable
leads to oscillatory behavior, as explained in section
\ref{subsec:prox:conv_properties}.
It should be noted that while in this thesis proximal decoding was
examined with respect to its performance in \ac{AWGN} channels, in
\cite{proximal_paper} it is presented as a method applicable to non-trivial
@ -279,19 +283,19 @@ broadening its usefulness beyond what is shown here.
The timing requirements of the decoding algorithms are visualized in figure
\ref{fig:comp:time}.
The datapoints have been generated by evaluating the metadata from \ac{FER}
and \ac{BER} simulations using the parameters mentioned earlier when
and \ac{BER} simulations and using the parameters mentioned earlier when
discussing the decoding performance.
The codes considered are the same as in sections \ref{subsec:prox:comp_perf}
and \ref{subsec:admm:comp_perf}.
While the \ac{ADMM} implementation seems to be faster the the proximal
decoding and improved proximal decoding implementations, infering some
While the \ac{ADMM} implementation seems to be faster than the proximal
decoding and improved proximal decoding implementations, inferring some
general behavior is difficult in this case.
This is because of the comparison of actual implementations, making the
results dependent on factors such as the grade of optimization of each of the
implementations.
Nevertheless, the run time of both the proximal decoding and the \ac{LP}
decoding using \ac{ADMM} implementations is similar and both are
reasonably performant, owing to the parallelisable structure of the
decoding using \ac{ADMM} implementations is similar, and both are
reasonably performant, owing to the parallelizable structure of the
algorithms.
%
\begin{figure}[h]
@ -328,8 +332,6 @@ algorithms.
\label{fig:comp:time}
\end{figure}%
%
\footnotetext{asdf}
%
\begin{figure}[h]
\centering
@ -572,7 +574,7 @@ algorithms.
\addlegendentry{\acs{LP} decoding using \acs{ADMM}}
\addlegendimage{RoyalPurple, line width=1pt, mark=*, solid}
\addlegendentry{\acs{BP} (20 iterations)}
\addlegendentry{\acs{BP} (200 iterations)}
\addlegendimage{Black, line width=1pt, mark=*, solid}
\addlegendentry{\acs{ML} decoding}
@ -580,8 +582,8 @@ algorithms.
\end{tikzpicture}
\end{subfigure}
\caption{Comparison of decoding performance between proximal decoding and \ac{LP} decoding
using \ac{ADMM}}
\caption{Comparison of decoding performance of the different decoder
implementations for various codes}
\label{fig:comp:prox_admm_dec}
\end{figure}

22
latex/thesis/chapters/conclusion.tex
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@ -4,15 +4,15 @@
In the context of this thesis, two decoding algorithms were considered:
proximal decoding and \ac{LP} decoding using \ac{ADMM}.
The two algorithms were first analyzed individually, before comparing them
based on simulation results as well as their theoretical structure.
based on simulation results as well as on their theoretical structure.
For proximal decoding, the effect of each parameter on the behavior of the
decoder was examined, leading to an approach to choosing the value of each
of the parameters.
decoder was examined, leading to an approach to optimally choose the value
of each parameter.
The convergence properties of the algorithm were investigated in the context
of the relatively high decoding failure rate, to derive an approach to correct
possible wrong components of the estimate.
Based on this approach, an improvement over proximal decoding was suggested,
possibly wrong components of the estimate.
Based on this approach, an improvement of proximal decoding was suggested,
leading to a decoding gain of up to $\SI{1}{dB}$, depending on the code and
the parameters considered.
@ -22,7 +22,7 @@ The decomposable nature arising from the relocation of the constraints into
the objective function itself was recognized as the major driver in enabling
an efficient implementation of the decoding algorithm.
Based on simulation results, general guidelines for choosing each parameter
were again derived.
were derived.
The decoding performance, in form of the \ac{FER}, of the algorithm was
analyzed, observing that \ac{LP} decoding using \ac{ADMM} nearly reaches that
of \ac{BP}, staying within approximately $\SI{0.5}{dB}$ depending on the code
@ -30,7 +30,7 @@ in question.
Finally, strong parallels were discovered with regard to the theoretical
structure of the two algorithms, both in the constitution of their respective
objective functions as in the iterative approaches used to minimize them.
objective functions as well as in the iterative approaches used to minimize them.
One difference noted was the approximate nature of the minimization in the
case of proximal decoding, leading to the constraints never being truly
satisfied.
@ -38,7 +38,7 @@ In conjunction with the alternating minimization with respect to the same
variable, leading to oscillatory behavior, this was identified as
a possible cause of its comparatively worse decoding performance.
Furthermore, both algorithms were expressed as message passing algorithms,
justifying their similar computational performance.
illustrating their similar computational performance.
While the modified proximal decoding algorithm presented in section
\ref{sec:prox:Improved Implementation} shows some promising results, further
@ -46,6 +46,12 @@ investigation is required to determine how different choices of parameters
affect the decoding performance.
Additionally, a more mathematically rigorous foundation for determining the
potentially wrong components of the estimate is desirable.
A different method to improve proximal decoding might be to use
moment-based optimization techniques such as \textit{Adam} \cite{adam}
to try to mitigate the effect of local minima introduced in the objective
function as well as the adversarial structure of the minimization when employing
proximal decoding.
Another area benefiting from future work is the expansion of the \ac{ADMM}
based \ac{LP} decoder into a decoder approximating \ac{ML} performance,
using \textit{adaptive \ac{LP} decoding}.

25
latex/thesis/chapters/introduction.tex
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@ -9,15 +9,17 @@ popular due to being able to reach arbitrarily small probabilities of error
at code rates up to the capacity of the channel \cite[Sec. II.B.]{mackay_rediscovery},
while retaining a structure that allows for very efficient decoding.
While the established decoders for \ac{LDPC} codes, such as \ac{BP} and the
\textit{min-sum algorithm}, offer reasonable decoding performance, they are suboptimal
in most cases and exhibit an \textit{error floor} for high \acp{SNR},
making them unsuitable for applications with extreme reliability requirements.
\textit{min-sum algorithm}, offer good decoding performance, they are suboptimal
in most cases and exhibit an \textit{error floor} for high \acp{SNR}
\cite[Sec. 15.3]{ryan_lin_2009}, making them unsuitable for applications
with extreme reliability requirements.
Optimization based decoding algorithms are an entirely different way of approaching
the decoding problem.
The initial introduction of optimization techniques as a way of decoding binary
linear codes was conducted in Feldman's 2003 Ph.D. thesis and subsequent paper,
The first introduction of optimization techniques as a way of decoding binary
linear codes was conducted in Feldman's 2003 Ph.D. thesis and a subsequent paper,
establishing the field of \ac{LP} decoding \cite{feldman_thesis}, \cite{feldman_paper}.
There, the \ac{ML} decoding problem is approximated by a \textit{linear program},
There, the \ac{ML} decoding problem is approximated by a \textit{linear program}, i.e.,
a linear, convex optimization problem, which can subsequently be solved using
several different algorithms \cite{alp}, \cite{interior_point},
\cite{original_admm}, \cite{pdd}.
@ -27,8 +29,8 @@ of the \ac{MAP} decoding problem \cite{proximal_paper}.
The motivation behind applying optimization methods to channel decoding is to
utilize existing techniques in the broad field of optimization theory, as well
as find new decoding methods not suffering from the same disadvantages as
existing message passing based approaches, or exhibiting other desirable properties.
as to find new decoding methods not suffering from the same disadvantages as
existing message passing based approaches or exhibiting other desirable properties.
\Ac{LP} decoding, for example, comes with strong theoretical guarantees
allowing it to be used as a way of closely approximating \ac{ML} decoding
\cite[Sec. I]{original_admm},
@ -36,11 +38,14 @@ and proximal decoding is applicable to non-trivial channel models such
as \ac{LDPC}-coded massive \ac{MIMO} channels \cite{proximal_paper}.
This thesis aims to further the analysis of optimization based decoding
algorithms as well as verify and complement the considerations present in
algorithms as well as to verify and complement the considerations present in
the existing literature.
Specifically, the proximal decoding algorithm and \ac{LP} decoding using
the \ac{ADMM} \cite{original_admm} are explored within the context of
\ac{BPSK} modulated \ac{AWGN} channels.
Implementations of both decoding methods are produced, and based on simulation
results from those implementations the algorithms are examined and compared.
Approaches to determine the optimal value of each parameter are derived and
the computational and decoding performance of the algorithms is examined.
An improvement on proximal decoding is suggested, achieving up to 1 dB of gain,
depending on the parameters chosen and the code considered.

278
latex/thesis/chapters/lp_dec_using_admm.tex
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@ -5,14 +5,12 @@ This chapter is concerned with \ac{LP} decoding - the reformulation of the
decoding problem as a linear program.
More specifically, the \ac{LP} decoding problem is solved using \ac{ADMM}.
First, the general field of \ac{LP} decoding is introduced.
The application of \ac{ADMM} to the decoding problem is explained.
Some notable implementation details are mentioned.
The application of \ac{ADMM} to the decoding problem is explained and some
notable implementation details are mentioned.
Finally, the behavior of the algorithm is examined based on simulation
results.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{LP Decoding}%
\label{sec:lp:LP Decoding}
@ -547,7 +545,7 @@ parity-checks until a valid result is returned \cite[Sec. IV.]{alp}.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Decoding Algorithm}%
\section{Decoding Algorithm and Implementation}%
\label{sec:lp:Decoding Algorithm}
The \ac{LP} decoding formulation in section \ref{sec:lp:LP Decoding}
@ -689,7 +687,6 @@ handled at the same time.
This can also be understood by interpreting the decoding process as a message-passing
algorithm \cite[Sec. III. D.]{original_admm}, \cite[Sec. II. B.]{efficient_lp_dec_admm},
depicted in algorithm \ref{alg:admm}.
\todo{How are the variables being initialized?}
\begin{genericAlgorithm}[caption={\ac{LP} decoding using \ac{ADMM} interpreted
as a message passing algorithm\protect\footnotemark{}}, label={alg:admm},
@ -735,7 +732,7 @@ before the $\boldsymbol{z}_j$ and $\boldsymbol{u}_j$ update steps (lines 4 and
subsequently replacing $\boldsymbol{T}_j \tilde{\boldsymbol{c}}$ with the
computed value in the two updates \cite[Sec. 3.4.3]{distr_opt_book}.
The main computational effort in solving the linear program then amounts to
The main computational effort in solving the linear program amounts to
computing the projection operation $\Pi_{\mathcal{P}_{d_j}} \left( \cdot \right) $
onto each check polytope. Various different methods to perform this projection
have been proposed (e.g., in \cite{original_admm}, \cite{efficient_lp_dec_admm},
@ -743,14 +740,14 @@ have been proposed (e.g., in \cite{original_admm}, \cite{efficient_lp_dec_admm},
The method chosen here is the one presented in \cite{original_admm}.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Implementation Details}%
\label{sec:lp:Implementation Details}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\section{Implementation Details}%
%\label{sec:lp:Implementation Details}
The development process used to implement this decoding algorithm was the same
as outlined in section
\ref{sec:prox:Implementation Details} for proximal decoding.
At first, an initial version was implemented in Python, before repeating the
\ref{sec:prox:Decoding Algorithm} for proximal decoding.
First, an initial version was implemented in Python, before repeating the
process using C++ to achieve higher performance.
Again, the performance can be increased by reframing the operations in such
a way that the computation can take place primarily with element-wise
@ -788,9 +785,13 @@ expression to be rewritten as%
.\end{align*}
%
Defining%
\footnote{
In this case $d_1, \ldots, d_n$ refer to the degree of the variable nodes,
i.e., $d_i,\hspace{1mm}i\in\mathcal{I}$.
}
%
\begin{align*}
\boldsymbol{D} := \begin{bmatrix}
\boldsymbol{d} := \begin{bmatrix}
d_1 \\
\vdots \\
d_n
@ -800,13 +801,12 @@ Defining%
\hspace{5mm}%
\boldsymbol{s} := \sum_{j\in\mathcal{J}} \boldsymbol{T}_j^\text{T}
\left( \boldsymbol{z}_j - \boldsymbol{u}_j \right)
\end{align*}%
\todo{Rename $\boldsymbol{D}$}%
,\end{align*}%
%
the $\tilde{\boldsymbol{c}}$ update can then be rewritten as%
%
\begin{align*}
\tilde{\boldsymbol{c}} \leftarrow \boldsymbol{D}^{\circ \left(-1\right)} \circ
\tilde{\boldsymbol{c}} \leftarrow \boldsymbol{d}^{\circ \left(-1\right)} \circ
\left( \boldsymbol{s} - \frac{1}{\mu}\boldsymbol{\gamma} \right)
.\end{align*}
%
@ -831,7 +831,7 @@ while $\sum_{j\in\mathcal{J}} \lVert \boldsymbol{T}_j\tilde{\boldsymbol{c}}
\left( \boldsymbol{z}_j - \boldsymbol{u}_j \right) $
end for
for $i$ in $\mathcal{I}$ do
$\tilde{\boldsymbol{c}} \leftarrow \boldsymbol{D}^{\circ \left( -1\right)} \circ
$\tilde{\boldsymbol{c}} \leftarrow \boldsymbol{d}^{\circ \left( -1\right)} \circ
\left( \boldsymbol{s} - \frac{1}{\mu}\boldsymbol{\gamma} \right) $
end for
end while
@ -852,6 +852,12 @@ Subsequently, the decoding performance is observed and compared to that of
Finally, the computational performance of the implementation and time
complexity of the algorithm are studied.
As was the case in chapter \ref{chapter:proximal_decoding} for proximal decoding,
the following simulation results are based on Monte Carlo simulations
and the BER and FER curves have been generated by producing at least 100
frame errors for each data point, except in cases where this is explicitly
specified otherwise.
\subsection{Choice of Parameters}
The first two parameters to be investigated are the penalty parameter $\mu$
@ -865,8 +871,8 @@ The code chosen for this examination is a (3,6) regular \ac{LDPC} code with
$n=204$ and $k=102$ \cite[\text{204.33.484}]{mackay_enc}.
When varying $\mu$, $\rho$ is set to 1 and when varying
$\rho$, $\mu$ is set to 5.
$K$ is set to 200 and $\epsilon_\text{dual}$ and $\epsilon_\text{pri}$ to
$10^{-5}$.
The maximum number of iterations $K$ is set to 200 and
$\epsilon_\text{dual}$ and $\epsilon_\text{pri}$ to $10^{-5}$.
The behavior that can be observed is very similar to that of the
parameter $\gamma$ in proximal decoding, analyzed in section
\ref{sec:prox:Analysis and Simulation Results}.
@ -1004,7 +1010,7 @@ run time of the decoding process.
\label{fig:admm:mu_rho_iterations}
\end{figure}%
%
The same behavior can be observed when looking at a number of different codes,
The same behavior can be observed when looking at various different codes,
as shown in figure \ref{fig:admm:mu_rho_multiple}.
%
\begin{figure}[h]
@ -1205,22 +1211,22 @@ as shown in figure \ref{fig:admm:mu_rho_multiple}.
\label{fig:admm:mu_rho_multiple}
\end{figure}
To get an estimate for the parameter $K$, the average error during decoding
can be used.
To get an estimate for the maximum number of iterations $K$ necessary,
the average error during decoding can be used.
This is shown in figure \ref{fig:admm:avg_error} as an average of
$\SI{100000}{}$ decodings.
$\mu$ is set to 5 and $\rho$ is set to $1$ and the rest of the parameters are
again chosen as $K=200, \epsilon_\text{pri}=10^{-5}$ and $ \epsilon_\text{dual}=10^{-5}$.
Similarly to the results in section
\ref{sec:prox:Analysis and Simulation Results}, a dip is visible around the
$20$ iteration mark.
This is due to the fact that as the number of iterations increases
again chosen as $\epsilon_\text{pri}=10^{-5}$ and
$\epsilon_\text{dual}=10^{-5}$.
Similarly to the results in section \ref{subsec:prox:choice}, a dip is
visible around the $20$ iteration mark.
This is due to the fact that as the number of iterations increases,
more and more decodings converge, leaving only the mistaken ones to be
averaged.
The point at which the wrong decodings start to become dominant and the
decoding performance does not increase any longer is largely independent of
the \ac{SNR}, allowing the value of $K$ to be chosen without considering the
\ac{SNR}.
the \ac{SNR}, allowing the maximum number of iterations to be chosen without
considering the \ac{SNR}.
\begin{figure}[h]
\centering
@ -1275,7 +1281,8 @@ These are both set to the same value $\epsilon$.
The effect of their value on the decoding performance is visualized in figure
\ref{fig:admm:epsilon}.
All parameters except $\epsilon_\text{pri}$ and $\epsilon_\text{dual}$ are
kept constant, with $K=200$, $\mu=5$, $\rho=1$ and $E_b / N_0 = \SI{4}{dB}$.
kept constant, with $\mu=5$, $\rho=1$ and $E_b / N_0 = \SI{4}{dB}$ and
performing a maximum of 200 iterations.
A lower value for the tolerance initially leads to a dramatic decrease in the
\ac{FER}, this effect fading as the tolerance becomes increasingly lower.
@ -1309,9 +1316,9 @@ In conclusion, the parameters $\mu$ and $\rho$ should be chosen comparatively
small and large, respectively, to reduce the average runtime of the decoding
process, while keeping them within a certain range as to not compromise the
decoding performance.
The maximum number of iterations $K$ performed can be chosen independantly
The maximum number of iterations performed can be chosen independently
of the \ac{SNR}.
Finally, relatively small values should be given to the parameters
Finally, small values should be given to the parameters
$\epsilon_{\text{pri}}$ and $\epsilon_{\text{dual}}$ to achieve the lowest
possible error rate.
@ -1328,7 +1335,7 @@ the same as in \cite{original_admm}.
The two \ac{FER} curves are practically identical.
Also shown is the curve resulting from \ac{BP} decoding, performing
1000 iterations.
The two algorithms perform relatively similarly, coming within $\SI{0.5}{dB}$
The two algorithms perform relatively similarly, staying within $\SI{0.5}{dB}$
of one another.
\begin{figure}[h]
@ -1367,73 +1374,189 @@ of one another.
\label{fig:admm:results}
\end{figure}%
%
In figure \ref{fig:admm:ber_fer}, the \ac{BER} and \ac{FER} for \ac{LP} decoding
using\ac{ADMM} and \ac{BP} are shown for a (3, 6) regular \ac{LDPC} code with
$n=204$.
To ensure comparability, in both cases the number of iterations was set to
In figure \ref{fig:admm:bp_multiple}, \ac{FER} curves for \ac{LP} decoding
using \ac{ADMM} and \ac{BP} are shown for various codes.
To ensure comparability, in all cases the number of iterations was set to
$K=200$.
The values of the other parameters were chosen as $\mu = 5$, $\rho = 1$,
$\epsilon = 10^{-5}$ and $\epsilon=10^{-5}$.
Comparing figures \ref{fig:admm:results} and \ref{fig:admm:ber_fer} it is
apparent that the difference in decoding performance depends on the code being
Comparing the simulation results for the different codes, it is apparent that
the difference in decoding performance depends on the code being
considered.
More simulation results are presented in figure \ref{fig:comp:prox_admm_dec}
in section \ref{sec:comp:res}.
For all codes considered here, however, the performance of \ac{LP} decoding
using \ac{ADMM} comes close to that of \ac{BP}, again staying withing
approximately $\SI{0.5}{dB}$.
\begin{figure}[h]
\centering
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
grid=both,
xlabel={$\mu$}, ylabel={\acs{BER}},
xlabel={$E_b / N_0$ (dB)}, ylabel={FER},
ymode=log,
ymax=1.5, ymin=8e-5,
width=\textwidth,
height=0.75\textwidth,
ymax=1.5, ymin=3e-7,
]
\addplot[Turquoise, line width=1pt, mark=*]
table [col sep=comma, x=SNR, y=BER,
discard if not={mu}{5.0},
discard if gt={SNR}{4.5}]
{res/admm/ber_2d_20433484.csv};
\addplot[RoyalPurple, line width=1pt, mark=*]
table [col sep=comma, x=SNR, y=BER,
discard if gt={SNR}{4.5}]
{/home/andreas/bp_20433484.csv};
table [x=SNR, y=FER, col sep=comma, discard if not={mu}{3.0}]
%{res/hybrid/2d_ber_fer_dfr_963965.csv};
{res/admm/ber_2d_963965.csv};
\addplot [RoyalPurple, mark=*, line width=1pt]
table [x=SNR, y=FER, col sep=comma]
{res/generic/bp_963965.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 3, 6 \right)$-regular \ac{LDPC} code with $n=96, k=48$
\cite[\text{96.3.965}]{mackay_enc}}
\end{subfigure}%
\hfill%
\begin{subfigure}[c]{0.48\textwidth}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
grid=both,
xlabel={$\rho$}, ylabel={\acs{FER}},
xlabel={$E_b / N_0$ (dB)}, ylabel={FER},
ymode=log,
ymax=1.5, ymin=8e-5,
width=\textwidth,
height=0.75\textwidth,
ymax=1.5, ymin=3e-7,
]
\addplot[Turquoise, line width=1pt, mark=*]
table [col sep=comma, x=SNR, y=FER,
discard if not={mu}{5.0},
discard if gt={SNR}{4.5}]
{res/admm/ber_2d_20433484.csv};
\addplot[RoyalPurple, line width=1pt, mark=*]
table [col sep=comma, x=SNR, y=FER,
discard if gt={SNR}{4.5}]
{/home/andreas/bp_20433484.csv};
table [x=SNR, y=FER, col sep=comma, discard if not={mu}{3.0}]
{res/admm/ber_2d_bch_31_26.csv};
\addplot [RoyalPurple, mark=*, line width=1pt]
table [x=SNR, y=FER, col sep=comma]
{res/generic/bp_bch_31_26.csv};
\end{axis}
\end{tikzpicture}
\caption{BCH code with $n=31, k=26$}
\end{subfigure}%
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
grid=both,
xlabel={$E_b / N_0$ (dB)}, ylabel={FER},
ymode=log,
ymax=1.5, ymin=8e-5,
width=\textwidth,
height=0.75\textwidth,
]
\addplot[Turquoise, line width=1pt, mark=*]
table [x=SNR, y=FER, col sep=comma,
discard if not={mu}{3.0},
discard if gt={SNR}{5.5}]
{res/admm/ber_2d_20433484.csv};
\addplot [RoyalPurple, mark=*, line width=1pt]
table [x=SNR, y=FER, col sep=comma]
{res/generic/bp_20433484.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 3, 6 \right)$-regular \ac{LDPC} code with $n=204, k=102$
\cite[\text{204.33.484}]{mackay_enc}}
\end{subfigure}%
\hfill%
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
grid=both,
xlabel={$E_b / N_0$ (dB)}, ylabel={FER},
ymode=log,
ymax=1.5, ymin=8e-5,
width=\textwidth,
height=0.75\textwidth,
]
\addplot[Turquoise, line width=1pt, mark=*]
table [x=SNR, y=FER, col sep=comma, discard if not={mu}{3.0}]
{res/admm/ber_2d_20455187.csv};
\addplot [RoyalPurple, mark=*, line width=1pt,
discard if gt={SNR}{5}]
table [x=SNR, y=FER, col sep=comma]
{res/generic/bp_20455187.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 5, 10 \right)$-regular \ac{LDPC} code with $n=204, k=102$
\cite[\text{204.55.187}]{mackay_enc}}
\end{subfigure}%
\vspace{3mm}
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
grid=both,
xlabel={$E_b / N_0$ (dB)}, ylabel={FER},
ymode=log,
ymax=1.5, ymin=8e-5,
width=\textwidth,
height=0.75\textwidth,
]
\addplot[Turquoise, line width=1pt, mark=*]
table [x=SNR, y=FER, col sep=comma, discard if not={mu}{3.0}]
{res/admm/ber_2d_40833844.csv};
\addplot [RoyalPurple, mark=*, line width=1pt,
discard if gt={SNR}{3}]
table [x=SNR, y=FER, col sep=comma]
{res/generic/bp_40833844.csv};
\end{axis}
\end{tikzpicture}
\caption{$\left( 3, 6 \right)$-regular \ac{LDPC} code with $n=204, k=102$
\cite[\text{204.33.484}]{mackay_enc}}
\end{subfigure}%
\hfill%
\begin{subfigure}[t]{0.48\textwidth}
\centering
\begin{tikzpicture}
\begin{axis}[
grid=both,
xlabel={$E_b / N_0$ (dB)}, ylabel={FER},
ymode=log,
ymax=1.5, ymin=8e-5,
width=\textwidth,
height=0.75\textwidth,
]
\addplot[Turquoise, line width=1pt, mark=*]
table [x=SNR, y=FER, col sep=comma, discard if not={mu}{3.0}]
{res/admm/ber_2d_pegreg252x504.csv};
\addplot [RoyalPurple, mark=*, line width=1pt]
table [x=SNR, y=FER, col sep=comma,
discard if gt={SNR}{3}]
{res/generic/bp_pegreg252x504.csv};
\end{axis}
\end{tikzpicture}
\caption{LDPC code (progressive edge growth construction) with $n=504, k=252$
\cite[\text{PEGReg252x504}]{mackay_enc}}
\end{subfigure}%
\vspace{5mm}
\begin{subfigure}[t]{\textwidth}
\centering
@ -1441,26 +1564,23 @@ in section \ref{sec:comp:res}.
\begin{axis}[hide axis,
xmin=10, xmax=50,
ymin=0, ymax=0.4,
legend columns=3,
legend style={draw=white!15!black,legend cell align=left}]
legend columns=1,
legend cell align={left},
legend style={draw=white!15!black}]
\addlegendimage{Turquoise, line width=1pt, mark=*}
\addlegendentry{\acs{LP} decoding using \acs{ADMM}}
\addlegendimage{RoyalPurple, line width=1pt, mark=*}
\addlegendentry{BP (200 iterations)}
\addlegendimage{RoyalPurple, line width=1pt, mark=*, solid}
\addlegendentry{\acs{BP} (200 iterations)}
\end{axis}
\end{tikzpicture}
\end{subfigure}
\caption{Comparison of the decoding performance of \acs{LP} decoding using
\acs{ADMM} and \acs{BP}. (3,6) regular \ac{LDPC} code with $n = 204$, $k = 102$
\cite[\text{204.33.484}]{mackay_enc}}
\label{fig:admm:ber_fer}
\end{figure}%
In summary, the decoding performance of \ac{LP} decoding using \ac{ADMM} comes
close to that of \ac{BP}, their difference staying in the range of
approximately $\SI{0.5}{dB}$, depending on the code in question.
\caption{Comparison of the decoding performance of \ac{LP} decoding using \ac{ADMM}
and \ac{BP} for various codes}
\label{fig:admm:bp_multiple}
\end{figure}
\subsection{Computational Performance}
\label{subsec:admm:comp_perf}

71
latex/thesis/chapters/proximal_decoding.tex
View File

@ -1,10 +1,10 @@
\chapter{Proximal Decoding}%
\chapter{Proximal Decoding and Implementation}%
\label{chapter:proximal_decoding}
In this chapter, the proximal decoding algorithm is examined.
First, the algorithm itself is described.
Then, some interesting ideas concerning the implementation are presented.
Simulation results are shown, based on which the behavior of the
First, the algorithm itself is described and some useful considerations
concerning the implementation are presented.
Simulation results are shown, based on which behavior of the
algorithm is investigated for different codes and parameters.
Finally, an improvement on proximal decoding is proposed.
@ -60,7 +60,7 @@ as is often done, and has a rather unwieldy representation:%
%
In order to rewrite the prior \ac{PDF}
$f_{\tilde{\boldsymbol{X}}}\left( \tilde{\boldsymbol{x}} \right)$,
the so-called \textit{code-constraint polynomial} is introduced as:%
the so-called \textit{code-constraint polynomial} is introduced as%
%
\begin{align*}
h\left( \tilde{\boldsymbol{x}} \right) =
@ -92,7 +92,7 @@ This approximation can then be plugged into equation (\ref{eq:prox:vanilla_MAP})
and the likelihood can be rewritten using the negative log-likelihood
$L \left( \boldsymbol{y} \mid \tilde{\boldsymbol{x}} \right) = -\ln\left(
f_{\boldsymbol{Y} \mid \tilde{\boldsymbol{X}}}\left(
\boldsymbol{y} \mid \tilde{\boldsymbol{x}} \right) \right) $:%
\boldsymbol{y} \mid \tilde{\boldsymbol{x}} \right) \right) $ as%
%
\begin{align*}
\hat{\boldsymbol{x}} &= \argmax_{\tilde{\boldsymbol{x}} \in \mathbb{R}^{n}}
@ -122,7 +122,7 @@ and the decoding problem is reformulated to%
.\end{align*}
%
For the solution of the approximate \ac{MAP} decoding problem, using the
For the solution of the approximate \ac{MAP} decoding problem using the
proximal gradient method, the two parts of equation
(\ref{eq:prox:objective_function}) are considered separately:
the minimization of the objective function occurs in an alternating
@ -168,8 +168,8 @@ with larger $\gamma$, the constraint that $\gamma$ be small is important,
as it keeps the effect of $h\left( \tilde{\boldsymbol{x}} \right) $ on the landscape
of the objective function small.
Otherwise, unwanted stationary points, including local minima, are introduced.
The authors say that ``in practice, the value of $\gamma$ should be adjusted
according to the decoding performance.'' \cite[Sec. 3.1]{proximal_paper}.
The authors say that ``[\ldots] in practice, the value of $\gamma$ should be adjusted
according to the decoding performance'' \cite[Sec. 3.1]{proximal_paper}.
%The components of the gradient of the code-constraint polynomial can be computed as follows:%
%%
@ -232,7 +232,8 @@ $\left[ -\eta, \eta \right]^n$.
The iterative decoding process resulting from these considerations is
depicted in algorithm \ref{alg:prox}.
\begin{genericAlgorithm}[caption={Proximal decoding algorithm for an \ac{AWGN} channel},
\begin{genericAlgorithm}[caption={Proximal decoding algorithm for an \ac{AWGN} channel.
Based on Algorithm 1 in \protect\cite{proximal_paper}},
label={alg:prox}]
$\boldsymbol{s} \leftarrow \boldsymbol{0}$
for $K$ iterations do
@ -247,9 +248,9 @@ return $\boldsymbol{\hat{c}}$
\end{genericAlgorithm}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Implementation Details}%
\label{sec:prox:Implementation Details}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%\section{Implementation Details}%
%\label{sec:prox:Implementation Details}
The algorithm was first implemented in Python because of the fast development
process and straightforward debugging ability.
@ -324,8 +325,8 @@ $[-\eta, \eta]$ individually.
In this section, the general behavior of the proximal decoding algorithm is
analyzed.
The impact of the parameters $\gamma$, as well as $\omega$, $K$ and $\eta$ is
examined.
The impact of the parameters $\gamma$, as well as $\omega$, the maximum
number of iterations $K$ and $\eta$ is examined.
The decoding performance is assessed based on the \ac{BER} and the
\ac{FER} as well as the \textit{decoding failure rate} - the rate at which
the algorithm produces results that are not valid codewords.
@ -340,14 +341,14 @@ simulations.
The \ac{BER} and \ac{FER} curves in particular have been generated by
producing at least 100 frame errors for each data point, unless otherwise
stated.
\todo{Same text about monte carlo simulations and frame errors for admm}
\subsection{Choice of Parameters}
\label{subsec:prox:choice}
First, the effect of the parameter $\gamma$ is investigated.
Figure \ref{fig:prox:results} shows a comparison of the decoding performance
of the proximal decoding algorithm as presented by Wadayama et al. in
of the proximal decoding algorithm as presented by Wadayama et al.
\cite{proximal_paper} and the implementation realized for this work.
\noindent The \ac{BER} curves for three different choices of
$\gamma$ are shown, as well as the curve resulting from decoding
@ -498,7 +499,7 @@ are chosen for the decoding.
The \ac{SNR} is kept constant at $\SI{4}{dB}$.
The \ac{BER} exhibits similar behavior in its dependency on $\omega$ and
on $\gamma$: it is minimized when keeping the value within certain
bounds, without displaying a single clear optimum.
bounds, without displaying a single distinct optimum.
It is noteworthy that the decoder seems to achieve the best performance for
similar values of the two step sizes.
Again, this consideration applies to a multitude of different codes, as
@ -1074,7 +1075,7 @@ are minimized in an alternating manner by use of their gradients.
\end{figure}%
%
While the initial net movement is generally directed in the right direction
owing to the gradient of the negative log-likelihood, the final oscillation
owing to the gradient of the negative log-likelihood, the resulting oscillation
may well take place in a segment of space not corresponding to a valid
codeword, leading to the aforementioned non-convergence of the algorithm.
This also partly explains the difference in decoding performance when looking
@ -1085,7 +1086,7 @@ The higher the \ac{SNR}, the more likely the gradient of the negative
log-likelihood is to point to a valid codeword.
The common component of the two gradients then pulls the estimate closer to
a valid codeword before the oscillation takes place.
This explains why the decoding performance is so much better for higher
This explains why the decoding performance is significantly better for higher
\acp{SNR}.
Looking at figure \ref{fig:prox:gradients:h} it also becomes apparent why the
@ -1201,7 +1202,7 @@ $\SI{2.80}{GHz}$ and utilizing all cores.
\end{axis}
\end{tikzpicture}
\caption{Timing requirements of the proximal decoding imlementation}
\caption{Timing requirements of the proximal decoding implementation}
\label{fig:prox:time_comp}
\end{figure}%
%
@ -1212,14 +1213,14 @@ $\SI{2.80}{GHz}$ and utilizing all cores.
\label{sec:prox:Improved Implementation}
As mentioned earlier, frame errors seem to mainly stem from decoding failures.
Coupled with the fact that the \ac{BER} indicates so much better
Coupled with the fact that the \ac{BER} indicates significantly better
performance than the \ac{FER}, this leads to the assumption that only a small
number of components of the estimated vector may be responsible for an invalid
result.
If it was possible to limit the number of possibly wrong components of the
estimate to a small subset, an \ac{ML}-decoding step could be performed on
a limited number of possible results (``ML-in-the-List'' as it will
subsequently be called) to improve the decoding performance.
a limited number of possible results (``ML-in-the-list'', as it is called)
to improve the decoding performance.
This concept is pursued in this section.
First, a guideline must be found with which to evaluate the probability that
@ -1274,11 +1275,11 @@ The datapoints are taken from a single decoding operation.
Using this observation as a rule to determine the $N\in\mathbb{N}$ most
probably wrong bits, all variations of the estimate with those bits modified
can be generated.
An \ac{ML}-in-the-List step can then be performed to determine the
An \ac{ML}-in-the-list step can then be performed to determine the
most likely candidate.
This process is outlined in algorithm \ref{alg:prox:improved}.
Its only difference to algorithm \ref{alg:prox} is that instead of returning
the last estimate when no valid result is reached, an ML-in-the-List step is
the last estimate when no valid result is reached, an ML-in-the-list step is
performed.
\begin{genericAlgorithm}[caption={Improved proximal decoding algorithm},
@ -1293,12 +1294,12 @@ for $K$ iterations do
end if
end for
$\textcolor{KITblue}{\text{Find }N\text{ most probably wrong bits}}$
$\textcolor{KITblue}{\text{Generate variations } \boldsymbol{\tilde{c}}_l,\hspace{1mm}
l\in \mathbb{N}\text{ of } \boldsymbol{\hat{c}}\text{ with the }N\text{ bits modified}}$
$\textcolor{KITblue}{\text{Compute }d_H\left( \boldsymbol{ \tilde{c}}_l,
\boldsymbol{\hat{c}} \right) \text{ for all valid codewords } \boldsymbol{\tilde{c}}_l}$
$\textcolor{KITblue}{\text{Output }\boldsymbol{\tilde{c}}_l\text{ with lowest }
d_H\left( \boldsymbol{ \tilde{c}}_l, \boldsymbol{\hat{c}} \right)}$
$\textcolor{KITblue}{\text{Generate variations } \hat{\boldsymbol{c}}_l,\hspace{1mm}
l\in \mathbb{N}\text{ of } \hat{\boldsymbol{c}}\text{ with the }N\text{ bits modified}}$
$\textcolor{KITblue}{\text{Compute }d_H\left( \hat{\boldsymbol{c}}_l,
\hat{\boldsymbol{c}} \right) \text{ for all valid codewords } \hat{\boldsymbol{c}}_l}$
$\textcolor{KITblue}{\text{Output }\hat{\boldsymbol{c}}_l\text{ with lowest }
d_H\left( \hat{\boldsymbol{c}}_l, \hat{\boldsymbol{c}} \right)}$
\end{genericAlgorithm}
%\todo{Not hamming distance, correlation}
@ -1316,7 +1317,7 @@ datapoints at $\SI{6}{dB}$, $\SI{6.5}{dB}$ and $\SI{7}{dB}$ are
The gain seems to depend on the value of $\gamma$, as well as becoming more
pronounced for higher \ac{SNR} values.
This is to be expected, since with higher \ac{SNR} values the number of bit
errors decreases, making the correction of those errors in the ML-in-the-List
errors decreases, making the correction of those errors in the ML-in-the-list
step more likely.
In figure \ref{fig:prox:improved:comp} the decoding performance
between proximal decoding and the improved algorithm is compared for a number
@ -1461,7 +1462,7 @@ In some cases, a gain of up to $\SI{1}{dB}$ or higher can be achieved.
Interestingly, the improved algorithm does not have much different time
complexity than proximal decoding.
This is the case, because the ML-in-the-List step is only performed when the
This is the case, because the ML-in-the-list step is only performed when the
proximal decoding algorithm produces an invalid result, which in absolute
terms happens relatively infrequently.
This is illustrated in figure \ref{fig:prox:time_complexity_comp}, where the
@ -1504,7 +1505,7 @@ theoretical considerations.
%
In conclusion, the decoding performance of proximal decoding can be improved
by appending an ML-in-the-List step when the algorithm does not produce a
by appending an ML-in-the-list step when the algorithm does not produce a
valid result.
The gain can in some cases be as high as $\SI{1}{dB}$ and is achievable with
negligible computational performance penalty.

20
latex/thesis/chapters/theoretical_background.tex
View File

@ -1,13 +1,13 @@
\chapter{Theoretical Background}%
\label{chapter:theoretical_background}
In this chapter, the theoretical background necessary to understand this
work is given.
In this chapter, the theoretical background necessary to understand the
decoding algorithms examined in this work is given.
First, the notation used is clarified.
The physical layer is detailed - the used modulation scheme and channel model.
A short introduction to channel coding with binary linear codes and especially
\ac{LDPC} codes is given.
The established methods of decoding LPDC codes are briefly explained.
The established methods of decoding \ac{LDPC} codes are briefly explained.
Lastly, the general process of decoding using optimization techniques is described
and an overview of the utilized optimization methods is given.
@ -31,7 +31,7 @@ Additionally, a shorthand notation will be used, denoting a set of indices as%
\hspace{5mm} m < n, \hspace{2mm} m,n\in\mathbb{Z}
.\end{align*}
%
In order to designate elemen-twise operations, in particular the \textit{Hadamard product}
In order to designate element-wise operations, in particular the \textit{Hadamard product}
and the \textit{Hadamard power}, the operator $\circ$ will be used:%
%
\begin{alignat*}{3}
@ -45,7 +45,7 @@ and the \textit{Hadamard power}, the operator $\circ$ will be used:%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\section{Preliminaries: Channel Model and Modulation}
\section{Channel Model and Modulation}
\label{sec:theo:Preliminaries: Channel Model and Modulation}
In order to transmit a bit-word $\boldsymbol{c} \in \mathbb{F}_2^n$ of length
@ -82,7 +82,7 @@ conducting this process, whereby \textit{data words} are mapped onto longer
\textit{codewords}, which carry redundant information.
\Ac{LDPC} codes have become especially popular, since they are able to
reach arbitrarily small probabilities of error at code rates up to the capacity
of the channel \cite[Sec. II.B.]{mackay_rediscovery} while having a structure
of the channel \cite[Sec. II.B.]{mackay_rediscovery}, while having a structure
that allows for very efficient decoding.
The lengths of the data words and codewords are denoted by $k\in\mathbb{N}$
@ -97,7 +97,7 @@ the number of parity-checks:%
\boldsymbol{H}\boldsymbol{c}^\text{T} = \boldsymbol{0} \right\}
.\end{align*}
%
A data word $\boldsymbol{u} \in \mathbb{F}_2^k$ can be mapped onto a codword
A data word $\boldsymbol{u} \in \mathbb{F}_2^k$ can be mapped onto a codeword
$\boldsymbol{c} \in \mathbb{F}_2^n$ using the \textit{generator matrix}
$\boldsymbol{G} \in \mathbb{F}_2^{k\times n}$:%
%
@ -527,8 +527,8 @@ interpreted componentwise.}
%
where $\boldsymbol{x}, \boldsymbol{\gamma} \in \mathbb{R}^n$, $\boldsymbol{b} \in \mathbb{R}^m$
and $\boldsymbol{A}\in\mathbb{R}^{m \times n}$.
A technique called \textit{Lagrangian relaxation} \cite[Sec. 11.4]{intro_to_lin_opt_book}
can then be applied.
A technique called \textit{Lagrangian relaxation} can then be applied
\cite[Sec. 11.4]{intro_to_lin_opt_book}.
First, some of the constraints are moved into the objective function itself
and weights $\boldsymbol{\lambda}$ are introduced. A new, relaxed problem
is formulated as
@ -660,7 +660,7 @@ $\boldsymbol{A} = \begin{bmatrix}
\ldots &
\boldsymbol{A}_N
\end{bmatrix}$.
The minimization of each term can then happen in parallel, in a distributed
The minimization of each term can happen in parallel, in a distributed
fashion \cite[Sec. 2.2]{distr_opt_book}.
In each minimization step, only one subvector $\boldsymbol{x}_i$ of
$\boldsymbol{x}$ is considered, regarding all other subvectors as being

2
latex/thesis/thesis.tex
View File

@ -6,7 +6,7 @@
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\thesisTitle{Application o Optimization Algorithms for Channel Decoding}
\thesisTitle{Application of Optimization Algorithms for Channel Decoding}
\thesisType{Bachelor's Thesis}
\thesisAuthor{Andreas Tsouchlos}
\thesisAdvisor{Prof. Dr.-Ing. Laurent Schmalen}