## Math 5410 Coding Theory III

### III. Decoding Linear Codes

The usefulness of an error-correcting code would be greatly diminished if the decoding procedure was very time consuming. While the concept of decoding, i.e., finding the nearest codeword to the received vector, is simple enough, the algorithms for carrying this out can vary tremendously in terms of time and memory requirements. Usually, the best decoding algorithms are those designed for specific types of codes. We shall examine some algorithms which deal with the general class of linear codes and so, will not necessarily be the best for any particular code in this class.

When a codeword c is transmitted and vector r is received, the difference between the two is called the error vector e, i.e. r = c + e. If H is a parity-check matrix for the linear code C, then Hrt = H(c + e)t = Hct + Het = Het since Hct = 0 for any codeword. Hrt is called the syndrome of r.

If wt(e) <= 1 then the syndrome of r = Het is just a scalar multiple of a column of H. This observation leads to a simple decoding algorithm for 1-error correcting linear codes. First, calculate the syndrome of r. If the syndrome is 0, no error has occurred and r is a codeword. Otherwise, find the column of H which is a scalar multiple of the syndrome. If no such column exists then more than one error has occurred and the code can not correct it. If however, the syndrome is a times column j, say, then add the vector with -a in position j and 0's elsewhere to r. This corrects the error.

In the case of binary Hamming codes (which are 1-error correcting) this algorithm can be made even simpler. Since the parity-check matrix contains all the non-zero vectors of length n-k as columns, arrange these columns so that they are in numerical order as binary integers. A non-zero syndrome will then be the binary representation of the column where the error has occurred. This is called Hamming decoding.

Example: Consider the binary Hamming code with parity-check matrix

```                            1  0  1  0  1  0  1
H  =  0  1  1  0  0  1  1
0  0  0  1  1  1  1  .
```
Notice that, for example, column 6 is (0 1 1)t, representing the integer 0(20) + 1(21) + 1(22) = 6. Suppose that c = (0 1 1 1 1 0 0) is transmitted and r = (0 1 1 1 1 1 0) is received. The syndrome Hrt = (0 1 1)t and the binary representation of this vector reveals bit 6 to be in error.

This procedure does not work for codes which are more than single error correcting. To deal with the more general situation, we introduce an equivalence relation on the vectors of V(n,q) with respect to the code C. We say that vectors x and y are equivalent with respect to C if and only if, x - y is in C. This is easily checked to be an equivalence relation, and the equivalence classes (which form a partition of V(n,q)) are called the cosets of C. An alternative way of viewing the cosets of C is to note that a coset consists of all the vectors that have the same syndrome. Indeed, if x - y is in C, then x - y = c for some c in C. Thus, x = c + y and

Hxt = H(c + y)t = Hct + Hyt = Hyt.

Furthermore, if two vectors have the same syndrome then they are equivalent. If Hxt = Hyt,

0 = Hxt - Hyt = H(x - y)t,

and so, x - y is in C.

Example: The table below lists the cosets of the binary (5,2) - code whose generator matrix is given by,

```                        1  0  1  0  1
G =   0  1  1  1  0   .
```
The cosets are the rows in the table, with the first row being the code C (which is also a coset).
```                00000   10101   01110   11011
00001   10100   01111   11010
00010   10111   01100   11001
00100   10001   01010   11111
01000   11101   00110   10011
10000   00101   11110   01011
11000   01101   10110   00011
10010   00111   11100   01001
```
When the vectors of V(n,q) are arranged by cosets in this way it is called a standard array for the code. The parity-check matrix for this code is
```                            1  1  1  0  0
H =    0  1  0  1  0
1  0  0  0  1
```
and the syndromes for each row are given below
```                             000
001
010
100
110
101
011
111
```
In each coset we can select a vector of minimum weight (when there is a choice we can make the selection arbitrarily), this vector is called a coset leader. In the example above, the coset leaders were written in the first column of the standard array. We can use the coset leaders in a decoding algorithm. When a vector r is received, we find the coset that it is contained in (via the syndrome calculation) and then subtract the coset leader for that coset to obtain a codeword. This procedure is called syndrome decoding. That syndrome decoding gives the correct error correction is the content of the next theorem.

Theorem 4 : Let C be an (n,k) code over GF(q) with codewords cj, 0 <= j <= qk - 1. Let li, 0 <= i <= qn-k - 1 be a set of coset leaders for the cosets of this code. If r = li + ch then

d(r,ch) <= d(r,cj) for all j, 0 <= j <= qk - 1.

Proof: Expressing these distances in terms of weights we have,

d(r,ch) = d(li + ch,ch) = wt(li + ch - ch) = wt(li) and
d(r,cj) = d(li + ch,cj) = wt(li + ch - cj).

But li + ch - cj is in the same coset as li, and the coset leader has minimal weight.

Note that if the coset leader is unique then ch is the closest codeword (if it is not unique, then ch is still as close as any other codeword). We have the following result about uniqueness.

Theorem 5 : Let C be a linear code with minimum distance d. If x is any vector such that

then x is a unique element of minimum weight in its coset of C and hence is always a coset leader.

Proof: Suppose that there exists another vector y with the same weight as x in x's coset. Since x and y are in the same coset, x - y is in C, but

This contradicts the minimum distance of the code, unless x - y is the zero vector, i.e. x = y.

Example : In the previous example, the (5,2)-code had minimum distance 3 (the minimum non-zero weight of a codeword) and so the above theorem states that the zero vector and all vectors of weight 1 will be unique coset leaders as can easily be verified from the standard array.

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