Mathematics Department

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1 Mathematics Department Matthew Pressland Room V57 WT 27/8 Advanced Higher Mathematics for INFOTECH Exercise Sheet 2. Let C F 6 3 be the linear code defined by the generator matrix G = 2 2 (a) Find a generator matrix G in standard form for C. (b) Find a parity-check matrix H for C. (c) Find the minimum distance d min of C, and all codewords. Repeat (a) (c) for the linear code C F 5 4 defined by the generator matrix ( ) γ + γ + γ γ G = γ + γ γ Solution. First for the code in F 6 3 : (a) Performing elementary row operations does not change the row-span C of G. So, subtracting the second row from the first, multiplying the third row by 2, and reordering, we find the generator matrix 2 2 G = 2 for C, which is in standard form. (b) Using the matrix G = (I A), we compute the parity-check matrix 2 H = ( A T I) = (c) No column of H is zero, no two columns are multiples of each other (since any two columns have zeros in different positions) but the second column is a linear combination of the fourth and fifth, so d min = 3, this being the minimal number of linearly dependent columns.

2 Computing ag for each a F 3 3, we see that the codewords are, 22, 22,, 22, 22, 222, 22, 22,, 22, 22, 22, 2222, 2, 2, 222,, 2222, 222, 222, 22, 2222, 2, 222, 22, 22. We can check that the minimum weight of a non-zero codeword is 3, confirming that d min = 3. Now for the code in F 5 4 : (a) Multiplying both rows by + γ and then subtracting + γ times the second from the first gives the generator matrix ( ) G + γ γ γ = γ for C, which is in standard form. (b) From G, we compute the parity-check matrix + γ γ H = γ γ (c) As for the first code, we see from the columns of H that d min = 3. Computing ag for each a F 2 4, we see that the codewords are (,,,, ), (, γ, + γ,, γ), (, + γ,,, + γ), (,, γ,, ), (γ,, + γ, + γ, γ), (γ, + γ,, + γ, ), (γ, γ, γ, + γ, ), (γ,,, + γ, + γ), ( + γ, γ,,, + γ), ( + γ,, γ,, ), ( + γ,,,, ), ( + γ, + γ, + γ,, γ), (, + γ, γ, γ, ), (,,, γ, + γ), (,, + γ, γ, γ), (, γ,, γ, ). Again we can check that the minimum weight of a non-zero codeword is 3, confirming that d min = Find a (5, ) binary Hamming code in standard form: there is more than one possibility for this form! (a) Find the 5-bit codeword corresponding to the -bit data word. (b) For the received message, use nearest neighbour decoding to find the transmitted Hamming codeword, and the corresponding -bit data word. Solution. We have (5, ) = (2 m, 2 m m) for m = 4, so the code is specified by a 4 5 parity-check matrix. A possible matrix in standard form is H = (Any matrix over F 2 of the right size of the form ( A T I) with pairwise distinct columns will do.)

3 (a) For H = ( A T I) as above, the check symbols of the data word x = are computed by A T x T =, so the codeword corresponding to x is. (b) The syndrome of y = is Hy T = which is the 5th column of H. Thus the coset leader is e 5, and the transmitted codeword was. The data word is, obtained by deleting the check digits. 3. Define the scalar product on F n q by u v = u v + + u n v n = uv T F q for u, v F n q. Given a k-dimensional linear code C F n q, its dual code is the (n k)-dimensional linear code C = {u F n q u v = for all v C}. Let G and H be a generator and parity-check matrix for C, and show that H and G are generator and parity-check matrices for C. You may use without proof the fact that rank(a) = rank(a T ) for any matrix A. Solution. First we show that H is a generator matrix for C, i.e. the rows of H span C. First we claim that any element of this row span, of the form ah for a F n k q, is in C, by checking that ah v = for all v C. Any such v is of the form bg for b F k q, so we have ah bg = ah(bg) T =. since H is a parity check matrix for C and bg C. Thus ah C. Note that since, as we have shown, HG T b T = for all b F k q, we have HG T =. Now we need to show that any element of C is of the form ah for some a F n k q. The dimension of the image of the map Fq n k F n q, a ah, is rank(h T ) (since (ah) T = Ha T ), and rank(h T ) = rank(h), which is n k since H is a parity-check matrix. Since dim(c ) = n k, the image must be all of C, as required. Since C is a k-dimensional code, rank(g) = k, and so the dimension of the kernel of the map a Ga T is n k. Since GH T = (HG T ) T = T =, it follows from a Lemma in the lectures that G is a parity-check matrix for C. 4. Let C F 6 3 be the dual of the first code from Exercise. (a) Find a generator matrix, a parity-check matrix, and all codewords of C. (b) Describe all cosets of C, by finding a coset leader for each, and calculate the syndrome of each coset leader.

4 (c) If the received message is v = 2, which codeword in C was most likely sent? What about if the received message is w = 2? (In the event that there is not a unique most likely sent message, give all maximally likely possibilities.) Solution. (a) Using the solutions to Exercise and 3, we see that C has generator matrix 2 G = and parity-check matrix H = 2 2 We find the codewords by calculating ag for all a F 3 3, to obtain (b) The cosets are, 22, 2, 2,, 22, 222, 22, 2222, 2, 22, 222, 22,, 2, 2, 22, 222, 22, 22, 22, 222, 222, 222, 22, 22, u + C = {u + c c C } for u F 6 3. Since there are 27 codewords in C, and 729 = 27 2 elements of F 6 3, there are 27 cosets, corresponding to the 27 possible syndromes in F 3 3. To get started we can note that any two different elements of F6 3 of weight at most lie in different cosets: their difference has weight at most 2, and thus is not an element of C, in which non-zero elements have weight at least 3. Such elements must also be coset leaders because of their low weight (since every non-zero element of C has weight at least 3, an element of weight cannot be in the same coset as an element of weight, i.e. the coset C ). This gives us coset leaders: u =, S(u ) =, u 2 =, S(u 2 ) = 2, u 3 = 2, S(u 3 ) = 22, u 4 =, S(u 4 ) =, u 5 = 2, S(u 5 ) = 22, u 6 =, S(u 6 ) =, u 7 = 2, S(u 7 ) = 22, u 8 =, S(u 8 ) =, u 9 = 2, S(u 9 ) = 22, u =, S(u ) =, u = 2, S(u ) = 2, u 2 =, S(u 2 ) = 2, u 3 = 2, S(u 3 ) =. Now we can look for vectors with the remaining 4 possible syndromes, either by trial and error or solving linear equations. We also need to check that these elements are coset leaders; this will be automatic if they have weight 2, since we have already computed all of the cosets of vectors

5 of weight at most above. For example: u 4 =, S(u 4 ) =, u 5 = 2, S(u 5 ) = 2, u 6 = 2, S(u 6 ) = 2, u 7 = 2, S(u 7 ) = 2, u 8 =, S(u 8 ) = 2, u 9 =, S(u 9 ) =, u 2 =, S(u 2 ) = 2, u 2 =, S(u 2 ) = 2, u 22 = 22, S(u 22 ) = 22, u 23 =, S(u 23 ) = 2, u 24 =, S(u 24 ) = 2, u 25 = 2, S(u 25 ) = 2, u 26 = 22, S(u 27 ) = 22, u 27 = 2, S(u 27 ) = 222. (c) The syndrome of v is 22, so it lies in the coset with leader u 9 = 2. This is the unique element of the coset with minimal weight (since it has Hamming distance at least 2 from every non-zero element of C ). Thus the most likely sent message is v u 9 = 22. The syndrome of w is, so it lies in the coset with leader u 9 =. However, this is not the unique element of this coset with minimal weight; a vector u 9 + c with c C has weight 2 whenever the second and fifth entries of c are 2, and there are two other non-zero entries of c the codewords with this property are 222, 22 and 2222, giving other possible coset leaders 2, and 22 for u 9 + C. Thus the most likely sent messages are all of which are equally likely., 22, 2, 222, 5. (Optional) Let K be a field and f : K n K m be the linear map defined by f(x) = Ax for some m n matrix A with entries in K. Recalling that Ker f is a vector subspace of K n (see Exercise 3(a) on Sheet 2), prove that rank(a) + dim(ker f) = n. You may use, without proof, the following fact: if W is a vector subspace of V, then any basis of W may be extended to a basis of V. Solution. Write k = dim(ker f), let v,..., v k be a basis of Ker f, and extend this to a basis v,..., v k, v k+,..., v n of K n. We now wish to show that rank(a) = n k. By definition, rank(a) is the dimension of the image of f, so we should find a basis of this image with size n k; as a candidate, we have the vectors Av k+,..., Av n, which all lie in this image. To see that they form a basis, we need to check that they span the image, and are linearly independent. First we check that they span. Any element of the image is of the form f(x) = Ax for some x K n. Since v,..., v n is a basis, we can find (unique) λ,..., λ n K such that x = λ v + + λ n v n. Then by linearity Ax = A(λ v + + λ n v n ) = λ Av + + λ n Av n = λ k+ Av k+ + + λ n Av n

6 since v i Ker f, meaning Av i =, whenever i k. Thus Av k+,..., Av n span the image of f. Now we show that these vectors are linearly independent. Let λ k+,..., λ n K be such that λ k+ Av k+ + + λ n Av n =. Then we wish to show that λ k+ = = λ n =. By linearity, we have = λ k+ Av k+ + + λ n Av n = A(λ k+ v k+ + + λ n v n ), meaning, by definition, that λ k+ v k+ + +λ n v n lies in Ker f. Since v,..., v k is a basis of Ker f, there are (unique) λ,..., λ k K such that λ v + + λ k v k = λ k+ v k+ + + λ n v n, giving a linear dependence between v,..., v n. But these vectors are a basis of K n, so this linear dependence must be trivial, i.e. λ i = for all i n. In particular, we get λ k+ = = λ n =, as we wanted.