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CSE5313 Coding and information theory for data science (Lecture 6)

Recap

Vector spaces and subspaces over finite fields

\mathbb{F}^n is a vector space over \mathbb{F}.

With point-wise vector addition and scalar multiplication.

Example

\mathbb{F}_2^4 is a vector space over \mathbb{F}_2.

Let $v=\begin{pmatrix} 1 & 1 & 1 & 1 \end{pmatrix}$

Then v is a vector in \mathbb{F}_2^4 that's "orthogonal" to itself.

v\cdot v=1+1+1+1=4=0 in \mathbb{F}_2.

In general field, the dual space and space may intersect non-trivially.

Let V be a subspace of \mathbb{F}^n.

V is a subgroup of \mathbb{F}^n under vector addition (\mathbb{F}^n,+).

• Apply the theorem: If H is finite, non-empty, and closed under the operation of G, then H is a subgroup of G.

Proof

Since $H\subseteq G$, $H$ is non-empty and closed under the operation of $G$ and finite, then $H\leq G$.

left to show:

Associativity: inherited from G.

Unit element: 0\in H.

Consider a\in H, a,a^2,a^3,\cdots are in H. Since H is finite, there exists i,j\in\mathbb{N} such that a^i=a^j.

Then a^i=a^j\iff a^{i-j}=e\in H.

Inverses: a^{-1}\in H.

Automatically holds for unit element traversing.

Is every subgroup of \mathbb{F}^n a subspace?

Answer

No.

Consider F_4=\{0,1,x,x+1\} (field extension of \mathbb{F}_2 with p(x)=x).

F_4^2=\{(a,b):a,b\in F_4\}, \{(0,0),(1,1)\} is a subgroup of (F_4^2,+).

But the span of F_4\{(1,1)\} is \{(0,0),(1,1),(x,x),(x+1,x+1)\}\neq \{(0,0),(1,1)\}, which is not a subspace of F_4^2.

Cosets in this definition are called Affine subspaces.

V+a=\{v+a:v\in V\}\text{ for some }a\in \mathbb{F}^n

New content

Linear codes

A linear code \mathcal{C} is a subspace of \mathbb{F}^n over \mathbb{F}.

• The dimension of \mathcal{C} is denoted by k.
• The minimum Hamming distance of \mathcal{C} is denoted by d.
• Notation \mathcal{C}= [n,k,d]_{\mathbb{F}}.

Two equivalent ways to constructing a linear code:

• A generator matrix G\in \mathbb{F}^{k\times n} with k rows and n columns.

  
  \mathcal{C}=\{xG:x\in \mathbb{F}^k\}
  

  • The left image of G is \mathcal{C}.
  • The rows of G are a basis for \mathcal{C}.

• A parity check matrix H\in \mathbb{F}^{(n-k)\times n} with (n-k) rows and n columns.

  
  \mathcal{C}=\{c\in \mathbb{F}^n:Hc^T=0\}
  

  • The right kernel of H is \mathcal{C}.
  • Multiplying c^T by H "checks" if c\in \mathcal{C}.

Encoding of linear codes

Reminder:

• Encoding is the process of mapping a message u\in \mathbb{F}^k to a codeword c\in \mathcal{C}\subseteq \mathbb{F}^n.

E: \mathbb{F}^k\to \mathcal{C} is a linear map.

Let \mathcal{C}= [n,k,d]_{\mathbb{F}} be a linear code with generator matrix G\in \mathbb{F}^{k\times n}.

• Encoding is given by E(x)=xG.
• It is injective (1-1). Suppose otherwise, then there exists x_1,x_2\in \mathbb{F}^k such that x_1G=x_2G. Then x_1G-x_2G=0\implies (x_1-x_2)G=0\implies x_1-x_2=0\implies x_1=x_2. Therefore, E is injective.

So linear codes implies linear encoding: E(x)+E(y)=E(x+y).

Systematic codes

Fact: Every G\in \mathbb{F}^{k\times n} can be brought to the form G_{sys}=(I|A) by

• Row operations.
• Permutation of columns.

Fact \{xG|x\in \mathbb{F}^k\} and \{xG_{sys}|x\in \mathbb{F}^k\} are equivalent.

• Same length n.
• Same dimension k.
• Same minimum Hamming distance d.

Encoding a systematic code:

• The input is a part of the output.
• Efficient encoding
• Immediate decoding (if no errors).

Codes, cosets, encoding, decoding

Linear code [n,k,d]_{\mathbb{F}} is a k dimensional subspace of \mathbb{F}^n.

Size of the code is |\mathbb{F}|^k.

Encoding: x\to xG.

Decoding: (y+e)\to x, y=xG.

Use syndrome to identify which coset \mathcal{C}_i that the noisy-code to \mathcal{C}_i+e belongs to.

H(y+e)^T=H(y+e)=Hx+He=He

Syndrome decoding

• Heavily depends onn the linear structure of the code.

Linear code \mathcal{C}= [n,k,d]_{\mathbb{F}} is a $k$-dimensional subspace of (\mathbb{F}^n,+).

Shift of Linear code [n,k,d]_{\mathbb{F}} is a $k$-dimensional affine subspace of \mathbb{F}^n.

All cosets of the same size

If w_H(e)\leq \lfloor \frac{d-1}{2}\rfloor, then it is possible to extract y from y+e.

by syndrome decoding, we can do better than exhaustive search.

Idea:

Let y+e belogns to the coset \mathcal{C}+e.

Moreover,$y_1+e$ and y_2+e are in the same coset.

Standard Array

Let \mathcal{C}= [n,k,d]_{\mathbb{F}} and denote |F|=q.

• Then |\mathcal{C}|=q^k.
• The number of cosets is q^{n-k}.

Then we arrange all q^n elements of \mathbb{F}^n into a q^{n-k}\times q^k array.

• So that every row is a coset (including \mathcal{C} itself)
• Lightest word in each cosets on the leftmost column

Example

Let \mathbb{F}=\mathbb{Z}_2 and C=\{xG|x\in \mathbb{F}_2\}

G=\begin{pmatrix}
1 & 0 & 1 & 1 & 0\\
0 & 1 & 1 & 0 & 1
\end{pmatrix}

So \mathcal{C}=\{00000,10110,01011,11101\}.

Then G=[5,2,3]_2.

The standard array is:

First row is \mathcal{C}.

Second row is \mathcal{C}+(00001),

Third row is \mathcal{C}+(00010).

Fourth row is \mathcal{C}+(00100).

00000	10110	01011	11101
00001	10111	01010	11100
00010	10100	01001	11110
00100	10010	01101	11000

Any two elements in a row are of the form y_1'=y_1+e and y_2'=y_2+e for some e\in \mathbb{F}^n.

Same syndrome if H(y_1'+e)^T=H(y_2'+e)^T.

Entries in different rows have different syndrome.

Proof

Choose the lightest word in each coset on the leftmost column.

Time complexity: O(n(n-k)). Space complexity: n|F|^n space.

Compare with exhaustive search: Time: O(|F|^n).

Syndrome decoding - Intuition

Given y', we identify the set \mathcal{C} + e to which y' belongs by computing the syndrome.

• We identify e as the coset leader (leftmost entry) of the row \mathcal{C} + e.
• We output the codeword in \mathcal{C} which is closest (c') by subtracting e from y'.

Syndrome decoding - Formal

Given y'\in \mathbb{F}^n, we identify the set \mathcal{C}+e to which y' belongs by computing the syndrome.

We identify e as the coset leader (leftmost entry) of the row \mathcal{C}+e.

We output the codeword in \mathcal{C} which is closest (example c_3) by subtracting e from y'.