## Section 8.4 Efficient Decoding

We are now at the stage where we are able to generate linear codes that detect and correct errors fairly easily, but it is still a time-consuming process to decode a received \(n\)-tuple and determine which is the closest codeword, because the received \(n\)-tuple must be compared to each possible codeword to determine the proper decoding. This can be a serious impediment if the code is very large.

### Example 8.35.

Given the binary matrix

and the \(5\)-tuples \({\mathbf x} = (11011)^\transpose\) and \({\mathbf y} = (01011)^\transpose\text{,}\) we can compute

Hence, \({\mathbf x}\) is a codeword and \({\mathbf y}\) is not, since \({\mathbf x}\) is in the null space and \({\mathbf y}\) is not. Notice that \(H{\mathbf y}\) is identical to the first column of \(H\text{.}\) In fact, this is where the error occurred. If we flip the first bit in \({\mathbf y}\) from \(0\) to \(1\text{,}\) then we obtain \({\mathbf x}\text{.}\)

If \(H\) is an \(m \times n\) matrix and \({\mathbf x} \in {\mathbb Z}_2^n\text{,}\) then we say that the syndrome of \({\mathbf x}\) is \(H{\mathbf x}\text{.}\) The following proposition allows the quick detection and correction of errors.

### Proposition 8.36.

Let the \(m \times n\) binary matrix \(H\) determine a linear code and let \({\mathbf x}\) be the received \(n\)-tuple. Write \({\mathbf x}\) as \({\mathbf x} = {\mathbf c} +{\mathbf e}\text{,}\) where \({\mathbf c}\) is the transmitted codeword and \({\mathbf e}\) is the transmission error. Then the syndrome \(H{\mathbf x}\) of the received codeword \({\mathbf x}\) is also the syndrome of the error \({\mathbf e}\text{.}\)

### Proof.

The proof follows from the fact that

This proposition tells us that the syndrome of a received word depends solely on the error and not on the transmitted codeword. The proof of the following theorem follows immediately from Proposition 8.36 and from the fact that \(H{\mathbf e}\) is the \(i\)th column of the matrix \(H\text{.}\)

### Theorem 8.37.

Let \(H \in {\mathbb M}_{ m \times n} ( {\mathbb Z}_2)\) and suppose that the linear code corresponding to \(H\) is single error-correcting. Let \({\mathbf r}\) be a received \(n\)-tuple that was transmitted with at most one error. If the syndrome of \({\mathbf r}\) is \({\mathbf 0}\text{,}\) then no error has occurred; otherwise, if the syndrome of \({\mathbf r}\) is equal to some column of \(H\text{,}\) say the \(i\)th column, then the error has occurred in the \(i\)th bit.

### Example 8.38.

Consider the matrix

and suppose that the \(6\)-tuples \({\mathbf x} = (111110)^\transpose\text{,}\) \({\mathbf y} = (111111)^\transpose\text{,}\) and \({\mathbf z} = (010111)^\transpose\) have been received. Then

Hence, \({\mathbf x}\) has an error in the third bit and \({\mathbf z}\) has an error in the fourth bit. The transmitted codewords for \({\mathbf x}\) and \({\mathbf z}\) must have been \((110110)\) and \((010011)\text{,}\) respectively. The syndrome of \({\mathbf y}\) does not occur in any of the columns of the matrix \(H\text{,}\) so multiple errors must have occurred to produce \({\mathbf y}\text{.}\)

### Subsection Coset Decoding

We can use group theory to obtain another way of decoding messages. A linear code \(C\) is a subgroup of \({\mathbb Z}_2^n\text{.}\) Coset or standard decoding uses the cosets of \(C\) in \({\mathbb Z}_2^n\) to implement maximum-likelihood decoding. Suppose that \(C\) is an \((n,m)\)-linear code. A coset of \(C\) in \({\mathbb Z}_2^n\) is written in the form \({\mathbf x} + C\text{,}\) where \({\mathbf x} \in {\mathbb Z}_2^n\text{.}\) By Lagrange's Theorem (Theorem 6.10), there are \(2^{n-m}\) distinct cosets of \(C\) in \({\mathbb Z}_2^n\text{.}\)

#### Example 8.39.

Let \(C\) be the \((5,3)\)-linear code given by the parity-check matrix

The code consists of the codewords

There are \(2^{5-2} = 2^3\) cosets of \(C\) in \({\mathbb Z}_2^5\text{,}\) each with order \(2^2 =4\text{.}\) These cosets are listed in Table 8.40.

Coset | Coset |

Representative | |

\(C\) | \((00000) (01101) (10011) (11110)\) |

\((10000) + C\) | \((10000) (11101) (00011) (01110)\) |

\((01000) + C\) | \((01000) (00101) (11011) (10110)\) |

\((00100) + C\) | \((00100) (01001) (10111) (11010)\) |

\((00010) + C\) | \((00010) (01111) (10001) (11100)\) |

\((00001) + C\) | \((00001) (01100) (10010) (11111)\) |

\((10100) + C\) | \((00111) (01010) (10100) (11001)\) |

\((00110) + C\) | \((00110) (01011) (10101) (11000)\) |

Our task is to find out how knowing the cosets might help us to decode a message. Suppose that \({\mathbf x}\) was the original codeword sent and that \({\mathbf r}\) is the \(n\)-tuple received. If \({\mathbf e}\) is the transmission error, then \({\mathbf r} = {\mathbf e} + {\mathbf x}\) or, equivalently, \({\mathbf x} = {\mathbf e} + {\mathbf r}\text{.}\) However, this is exactly the statement that \({\mathbf r}\) is an element in the coset \({\mathbf e} + C\text{.}\) In maximum-likelihood decoding we expect the error \({\mathbf e}\) to be as small as possible; that is, \({\mathbf e}\) will have the least weight. An \(n\)-tuple of least weight in a coset is called a coset leader. Once we have determined a coset leader for each coset, the decoding process becomes a task of calculating \({\mathbf r} + {\mathbf e}\) to obtain \({\mathbf x}\text{.}\)

#### Example 8.41.

In Table 8.40, notice that we have chosen a representative of the least possible weight for each coset. These representatives are coset leaders. Now suppose that \({\mathbf r} = (01111)\) is the received word. To decode \({\mathbf r}\text{,}\) we find that it is in the coset \((00010) + C\text{;}\) hence, the originally transmitted codeword must have been \((01101) = (01111) + (00010)\text{.}\)

A potential problem with this method of decoding is that we might have to examine every coset for the received codeword. The following proposition gives a method of implementing coset decoding. It states that we can associate a syndrome with each coset; hence, we can make a table that designates a coset leader corresponding to each syndrome. Such a list is called a decoding table.

Syndrome | Coset Leader |

\((000)\) | \((00000)\) |

\((001)\) | \((00001)\) |

\((010)\) | \((00010)\) |

\((011)\) | \((10000)\) |

\((100)\) | \((00100)\) |

\((101)\) | \((01000)\) |

\((110)\) | \((00110)\) |

\((111)\) | \((10100)\) |

#### Proposition 8.43.

Let \(C\) be an \((n,k)\)-linear code given by the matrix \(H\) and suppose that \({\mathbf x}\) and \({\mathbf y}\) are in \({\mathbb Z}_2^n\text{.}\) Then \({\mathbf x}\) and \({\mathbf y}\) are in the same coset of \(C\) if and only if \(H{\mathbf x} = H{\mathbf y}\text{.}\) That is, two \(n\)-tuples are in the same coset if and only if their syndromes are the same.

#### Proof.

Two \(n\)-tuples \({\mathbf x}\) and \({\mathbf y}\) are in the same coset of \(C\) exactly when \({\mathbf x} - {\mathbf y} \in C\text{;}\) however, this is equivalent to \(H({\mathbf x} - {\mathbf y}) = 0\) or \(H {\mathbf x} = H{\mathbf y}\text{.}\)

#### Example 8.44.

Table 8.42 is a decoding table for the code \(C\) given in Example 8.39. If \({\mathbf x} = (01111)\) is received, then its syndrome can be computed to be

Examining the decoding table, we determine that the coset leader is \((00010)\text{.}\) It is now easy to decode the received codeword.

Given an \((n,k)\)-block code, the question arises of whether or not coset decoding is a manageable scheme. A decoding table requires a list of cosets and syndromes, one for each of the \(2^{n - k}\) cosets of \(C\text{.}\) Suppose that we have a \((32, 24)\)-block code. We have a huge number of codewords, \(2^{24}\text{,}\) yet there are only \(2^{32 - 24} = 2^{8} = 256\) cosets.