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Lecture 3

Introduction to Vector Space Theory

Matrices

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Block codes: basic definitions An alphabet is a discrete (usually finite) set of

symbols.example: B = { 0; 1} is the binary alphabet

Definition: A block code of blocklength n over an

alphabet X is a nonempty set of n-tuples ofsymbols from X.

The n-tuples of the code are called codewords.

Codewords are vectors whose components aresymbols in X.

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Block codes: basic definitions Codewords of length n are typically generated

by encoding messages of k information bitsusing an invertible encoding function.

Number of codewords is M = 2k , Rate R = k/n

The rate is a dimensionless fraction; the fractionof transmitted symbols that carry information.

A code with blocklength n and rate k/n is calledan (n; k) code

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Linear Block Codes

matrixGeneratorG

(vector)wordmessagem

(vector)wordcode

,

=

c

where

Gmc

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Vector Space-Introduction

Ann-dimensional vector has a form

x = (x1, x2, x3, , xn ). The setRn ofn-dimensional vectors is a vector

space.

Any set Vis called a vector space if it containsobjects that behave like vectors:

ie, they add & multiply by scalars according to

certain rules. In particular, they must beclosedunder vector addition and scalar multiplication.

Butaddition &scalar multiplication need not be

defined conventionally!

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Contd

Let V denote the vector space.The addition on Vis vector addition.The scalar multiplicationcombines a scalar from a Field F and a vectorfrom V. Hence V is defined over a field F.

V must form a commutative group under addition For any element a in F and any element v in V,a.V is an element in V.

Distributive law- a.(u+v)=a.u+a.v Associative law- (a.b).v=a.(b.v)

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Contd.

Important vector spaces:

R, R2, R3,Rn with usual + andscalar multn. Mmn; the set of allm xn matrices

Pn; all polynomials of degree n Consider a vector space over binary fieldF2.Consider the sequence u=u0un-1 where the

ui sare from {0,1}.We can construct such 2n

n-tuples over F2.Let Vn denote this set. Vn is aVector space over F2

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Subspaces

A set Wof vectors isasubspace of vectorspace Vif and only ifW is a subset ofV andW is itself a vector space under the same

For any two vectors u,v W, (u+v) W.

For any element a in F and any u in W, a.umust be in W.

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Contd

To test ifWis a Subspace

We should, but need not, check all the propertiesof a vector space in W: most hold because Ws

vectors are also in the bigger vector space V.

But wemust check closure in W: linear

combinations of vectors in Wmust also lie in W.

This means thezero &additive inverses mustbe in Wtoo.

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Examples

Let u1,.,uk be a set of k vectors in V over

a field F. The set of all linear combinationsof u1,.,uk forms a subspace of V.

The set of polys of degree 2 or less is a

subspace of the set of polynomials of degree3 or less.

Theset of integers isnot a subspace ofR,because the set of scalars includes fractions,eg 1/2.

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Spanning Sets &Linear Independence

A set S = {u1,u2,.......,un} of vectors is said tospan avector space Vifevery vector in Vcan beexpressed as a linear combination of the vectors inS.

Ex:(x, y, z ) = x i + yj + z k, so every vector inR

3

isa linear combination of i, j & k.

If any vector in a set can be expressed as a

linear combination of the others, we call theset linearly dependent. If not, the set is linearlyindependent.

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Basis set

A set of linearly independent vectors is a

basis for a Vector space V if each vector inV

can be expressed in one and only one way as alinear combination of the set.

In any Vector space or subspace there exists atleast one set B of linearly independent vectorswhich span the space.

The no. of vectors in the Basis of a Vectorspace is the dimensionof the Vector space.

One example of a basis are the vectors

(1,0,,0), (0,1,,0),, (0,0, , 1).

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Orthogonality

Let u= and

v=

be two n-tuples in Vn. We define the inner product(dot product) as

u.v= where the multiplication and addition are

carried out in mod-2.. The inner product is a scalar. If u.v=0, then u and v

are said to be orthogonal to each other

The inner product has the following properties

(1) u.v=v.u

(2) u.(v+w)=u.v+u.W

(3)(au).v=a(u.v)

.

),.....,( 110 nuuu

),....,( 110 nvvv

0 0 1 1 1 1........ n nu v u v u v + + +

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MatricesA k x n matrix over F2 is a rectangular array with

k rows and n columns.

00 01 02 0, 1

10 11 12 1, 1

1,0 1,1 1,2 1, 1

.....

.....

. . . . .

. . . . .

.....

n

n

k k k k n

g g g gg g g g

G

g g g g

=

where each ijg 0 0i k and j n with

is an element from the binary field F2.

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G is also represented by its k rows

as0 0 1, ,..... kg g g

0

1

1

.

.

k

g

g

G

g

=

Each row of G is an n-tuple and each column is a k-tuple over F2.

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If k (with ) rows of G are linearlyindependent, then the 2k linear combinationsof of these rows form a k dimensionalsubspace of the vector spaceVn of all the n-

tuples over F2. This subspace is called therow space of G

Elementary row operations will not changethe row space of G

k n

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Let S be the row space of a k x n matrix G overF2whose rows are linearly independent. Let Sd be

the null space ofS. Then the dimension ofSd is

n-k. Consider (n-k) linearly independentvectorsin Sd. These vectors span Sd. We can form an

(n-k) x n matrix H as00 01 02 0, 10

10 11 12 1, 11

1,0 1,1 1,2 1, 11

.....

.....

. . . . ..

. . . . ..

.....

n

n

n k n k n k n k nn k

h h h hh

h h h hh

H

h h h hg

= =

The row space of H is Sd

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Since each row gi is a vector in S and each

row hj of H is a vector in Sd, the innerproduct of gi and hj must be zero. As therow space S of G is the null space of the

row space Sd of H, S is called the null space

or dual spaceof H.

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Theorem

For any k x n matrix G over F2, with k linearly

independent rows, there exists an (n-k) x nmatrix over the same field with (n-k) linearlyindependent rows such that for any row gi in

G and any hj in H, gi.hj = 0. The row space ofG is the null space of H and vice versa.

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