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In [[coding theory]], '''generalized minimum-distance (GMD) decoding''' provides an efficient [[algorithm]] for decoding [[concatenated code]]s, which is based on using an [[error]]s-and-[[Erasure code|erasures]] decoder for the [[outer code]].
A [[Concatenated error correction code#Decoding concatenated codes|naive decoding algorithm]] for concatenated codes can not be an optimal way of decoding because it does not take into account the information that [[maximum likelihood decoding]] (MLD) gives. In other words, in the naive algorithm, inner received [[Code word (communication)|codeword]]s are treated the same regardless of the difference between their [[hamming distance]]s. Intuitively, the outer decoder should place higher confidence in symbols whose inner [[code|encodings]] are close to the received word. [[David Forney]] in 1966 devised a better algorithm called generalized minimum distance (GMD) decoding which makes use of those information better. This method is achieved by measuring confidence of each received codeword, and erasing symbols whose confidence is below a desired value. And GMD decoding algorithm was one of the first examples of [[soft-decision decoder]]s. We will present three versions of the GMD decoding algorithm. The first two will be [[randomized algorithm]]s while the last one will be a [[deterministic algorithm]].
==Setup==
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# Run errors and erasure algorithm for <math>C_\text{out}</math> on <math>\mathbf{y}'' = (y_1'', \ldots, y_N'')</math>.
'''Theorem 1.''' ''Let y be a received word such that there exists a [[Code word (communication)|codeword]]'' <math>\mathbf{c} = (c_1,\cdots, c_N) \in C_\text{out}\circ{C_\text{in}} \subseteq [q^n]^N</math> ''such that'' <math>\Delta(\mathbf{c}, \mathbf{y}) < \tfrac{Dd}{2}</math>. ''Then the deterministic GMD algorithm outputs'' <math>\mathbf{c}</math>.
Note that a [[Concatenated codes|naive decoding algorithm for concatenated codes]] can correct up to <math>\tfrac{Dd}{4}</math> errors.
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'''Proof of lemma 1.''' For every <math>1 \le i \le N,</math> define <math>e_i = \Delta(y_i, c_i).</math> This implies that
Next for every <math>1 \le i \le N</math>, we define two [[indicator variable]]s:
X{_i^?} = 1 &\Leftrightarrow y_i'' = ? \\
X{_i^e} = 1 &\Leftrightarrow C_\text{in}(y_i'') \ne c_i \ \text{and} \ y_i'' \neq ?
\end{align}</math>
We claim that we are done if we can show that for every <math>1 \le i \le N</math>:
▲Clearly, by definition
▲:<math>e' = \sum_i X_i^e \quad \text{and} \quad s' = \sum_i X_i^?.</math>
▲Further, by the [[linear]]ity of expectation, we get
:<math>\mathbb{E}[2e' + s'] \leqslant \frac{2}{d}\sum_ie_i < D.</math>▼
To prove (2) we consider two cases: <math>i</math>-th block is correctly decoded ('''Case 1'''), <math>i</math>-th block is incorrectly decoded ('''Case 2'''):
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Further, by definition we have
'''Case 2:''' <math>(c_i \ne C_\text{in}(y_i'))</math>
In this case, <math>\mathbb{E}[X_i^?] = \tfrac{2\omega_i}{d}</math> and <math>\mathbb{E}[X_i^e] = \Pr[X_i^e = 1] = 1 - \tfrac{2\omega_i}{d}.</math>
Since <math>c_i \ne C_\text{in}(y_i'), e_i + \omega_i \geqslant d</math>. This follows
Finally, this implies
In the following sections, we will finally show that the deterministic version of the algorithm above can do unique decoding of <math>C_\text{out} \circ C_\text{in}</math> up to half its design distance.
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For the proof of '''[[Lemma (mathematics)|Lemma 1]]''', we only use the randomness to show that
In this version of the GMD algorithm, we note that
The second [[Equality (mathematics)|equality]] above follows from the choice of <math>\theta</math>. The proof of '''Lemma 1''' can be also used to show <math>\mathbb{E}[2e' + s'] < D</math> for version2 of GMD. In the next section, we will see how to get a deterministic version of the GMD algorithm by choosing <math>\theta</math> from a polynomially sized set as opposed to the current infinite set <math>[0, 1]</math>.
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Let <math>Q = \{0,1\} \cup \{{2\omega_1 \over d}, \ldots,{2\omega_N \over d}\}</math>. Since for each <math>i, \omega_i = \min(\Delta(\mathbf{y_i'}, \mathbf{y_i}), {d \over 2})</math>, we have
where <math>q_1 < \cdots < q_m</math> for some <math>m \le \left \lfloor \frac{d}{2} \right \rfloor</math>. Note that for every <math>\theta \in [q_i, q_{i+1}]</math>, the step 1 of the second version of randomized algorithm outputs the same <math>\mathbf{y}''.</math>. Thus, we need to consider all possible value of <math>\theta \in Q</math>. This gives the deterministic algorithm below.
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==See also==
==References==
{{Reflist}}
* [http://www.cs.washington.edu/education/courses/cse533/06au University of Washington – Venkatesan Guruswami]
{{DEFAULTSORT:Generalized minimum distance decoding}}
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