Misplaced Pages

This article may be too technical for most readers to understand. Please help improve it to make it understandable to non-experts, without removing the technical details. (March 2014) (Learn how and when to remove this message)

In coding theory, a cyclic code is a block code, where the circular shifts of each codeword gives another word that belongs to the code. They are error-correcting codes that have algebraic properties that are convenient for efficient error detection and correction.

Definition

Let ${\displaystyle {\mathcal {C}}}$ be a linear code over a finite field (also called Galois field) ${\displaystyle GF(q)}$ of block length ${\displaystyle n}$. ${\displaystyle {\mathcal {C}}}$ is called a cyclic code if, for every codeword ${\displaystyle c=(c_{1},\ldots ,c_{n})}$ from ${\displaystyle {\mathcal {C}}}$, the word ${\displaystyle (c_{n},c_{1},\ldots ,c_{n-1})}$ in ${\displaystyle GF(q)^{n}}$ obtained by a cyclic right shift of components is again a codeword. Because one cyclic right shift is equal to ${\displaystyle n-1}$ cyclic left shifts, a cyclic code may also be defined via cyclic left shifts. Therefore, the linear code ${\displaystyle {\mathcal {C}}}$ is cyclic precisely when it is invariant under all cyclic shifts.

Cyclic codes have some additional structural constraint on the codes. They are based on Galois fields and because of their structural properties they are very useful for error controls. Their structure is strongly related to Galois fields because of which the encoding and decoding algorithms for cyclic codes are computationally efficient.

Algebraic structure

Cyclic codes can be linked to ideals in certain rings. Let ${\displaystyle R=A/(x^{n}-1)}$ be a polynomial ring over the finite field ${\displaystyle A=GF(q)}$. Identify the elements of the cyclic code ${\displaystyle C}$ with polynomials in ${\displaystyle R}$ such that ${\displaystyle (c_{0},\ldots ,c_{n-1})}$ maps to the polynomial ${\displaystyle c_{0}+c_{1}x+\cdots +c_{n-1}x^{n-1}}$: thus multiplication by ${\displaystyle x}$ corresponds to a cyclic shift. Then ${\displaystyle C}$ is an ideal in ${\displaystyle R}$, and hence principal, since ${\displaystyle R}$ is a principal ideal ring. The ideal is generated by the unique monic element in ${\displaystyle C}$ of minimum degree, the generator polynomial ${\displaystyle g}$. This must be a divisor of ${\displaystyle x^{n}-1}$. It follows that every cyclic code is a polynomial code. If the generator polynomial ${\displaystyle g}$ has degree ${\displaystyle d}$ then the rank of the code ${\displaystyle C}$ is ${\displaystyle n-d}$.

The idempotent of ${\displaystyle C}$ is a codeword ${\displaystyle e}$ such that ${\displaystyle e^{2}=e}$ (that is, ${\displaystyle e}$ is an idempotent element of ${\displaystyle C}$) and ${\displaystyle e}$ is an identity for the code, that is ${\displaystyle e\cdot c=c}$ for every codeword ${\displaystyle c}$. If ${\displaystyle n}$ and ${\displaystyle q}$ are coprime such a word always exists and is unique; it is a generator of the code.

An irreducible code is a cyclic code in which the code, as an ideal is irreducible, i.e. is minimal in ${\displaystyle R}$, so that its check polynomial is an irreducible polynomial.

Examples

For example, if ${\displaystyle A=\mathbb {F} _{2}}$ and ${\displaystyle n=3}$, the set of codewords contained in cyclic code generated by ${\displaystyle (1,1,0)}$ is precisely

${\displaystyle ((0,0,0),(1,1,0),(0,1,1),(1,0,1))}$.

It corresponds to the ideal in ${\displaystyle \mathbb {F} _{2}/(x^{3}-1)}$ generated by ${\displaystyle (1+x)}$.

The polynomial ${\displaystyle (1+x)}$ is irreducible in the polynomial ring, and hence the code is an irreducible code.

The idempotent of this code is the polynomial ${\displaystyle x+x^{2}}$, corresponding to the codeword ${\displaystyle (0,1,1)}$.

Trivial examples

Trivial examples of cyclic codes are ${\displaystyle A^{n}}$ itself and the code containing only the zero codeword. These correspond to generators ${\displaystyle 1}$ and ${\displaystyle x^{n}-1}$ respectively: these two polynomials must always be factors of ${\displaystyle x^{n}-1}$.

Over ${\displaystyle GF(2)}$ the parity bit code, consisting of all words of even weight, corresponds to generator ${\displaystyle x+1}$. Again over ${\displaystyle GF(2)}$ this must always be a factor of ${\displaystyle x^{n}-1}$.

Quasi-cyclic codes and shortened codes

Before delving into the details of cyclic codes first we will discuss quasi-cyclic and shortened codes which are closely related to the cyclic codes and they all can be converted into each other.

Definition

Quasi-cyclic codes:

An ${\displaystyle (n,k)}$ quasi-cyclic code is a linear block code such that, for some ${\displaystyle b}$ which is coprime to ${\displaystyle n}$, the polynomial ${\displaystyle x^{b}c(x){\pmod {x^{n}-1}}}$ is a codeword polynomial whenever ${\displaystyle c(x)}$ is a codeword polynomial.

Here, codeword polynomial is an element of a linear code whose code words are polynomials that are divisible by a polynomial of shorter length called the generator polynomial. Every codeword polynomial can be expressed in the form ${\displaystyle c(x)=a(x)g(x)}$, where ${\displaystyle g(x)}$ is the generator polynomial. Any codeword ${\displaystyle (c_{0},..,c_{n-1})}$ of a cyclic code ${\displaystyle C}$ can be associated with a codeword polynomial, namely, ${\displaystyle \sum _{i=0}^{n-1}c_{i}*x^{i}}$. A quasi-cyclic code with ${\displaystyle b}$ equal to ${\displaystyle 1}$ is a cyclic code.

Definition

Shortened codes:

An ${\displaystyle (n,k)}$ linear code is called a proper shortened cyclic code if it can be obtained by deleting ${\displaystyle b}$ positions from an ${\displaystyle (n+b,k+b)}$ cyclic code.

In shortened codes information symbols are deleted to obtain a desired blocklength smaller than the design blocklength. The missing information symbols are usually imagined to be at the beginning of the codeword and are considered to be 0. Therefore, ${\displaystyle n}$−${\displaystyle k}$ is fixed, and then ${\displaystyle k}$ is decreased which eventually decreases ${\displaystyle n}$. It is not necessary to delete the starting symbols. Depending on the application sometimes consecutive positions are considered as 0 and are deleted.

All the symbols which are dropped need not be transmitted and at the receiving end can be reinserted. To convert ${\displaystyle (n,k)}$ cyclic code to ${\displaystyle (n-b,k-b)}$ shortened code, set ${\displaystyle b}$ symbols to zero and drop them from each codeword. Any cyclic code can be converted to quasi-cyclic codes by dropping every ${\displaystyle b}$th symbol where ${\displaystyle b}$ is a factor of ${\displaystyle n}$. If the dropped symbols are not check symbols then this cyclic code is also a shortened code.

For correcting errors

Cyclic codes can be used to correct errors, like Hamming codes as cyclic codes can be used for correcting single error. Likewise, they are also used to correct double errors and burst errors. All types of error corrections are covered briefly in the further subsections.

The (7,4) Hamming code has a generator polynomial ${\displaystyle g(x)=x^{3}+x+1}$. This polynomial has a zero in Galois extension field ${\displaystyle GF(8)}$ at the primitive element ${\displaystyle \alpha }$, and all codewords satisfy ${\displaystyle {\mathcal {C}}(\alpha )=0}$. Cyclic codes can also be used to correct double errors over the field ${\displaystyle GF(2)}$. Blocklength will be ${\displaystyle n}$ equal to ${\displaystyle 2^{m}-1}$ and primitive elements ${\displaystyle \alpha }$ and ${\displaystyle \alpha ^{3}}$ as zeros in the ${\displaystyle GF(2^{m})}$ because we are considering the case of two errors here, so each will represent one error.

The received word is a polynomial of degree ${\displaystyle n-1}$ given as ${\displaystyle v(x)=a(x)g(x)+e(x)}$

where ${\displaystyle e(x)}$ can have at most two nonzero coefficients corresponding to 2 errors.

We define the syndrome polynomial, ${\displaystyle S(x)}$ as the remainder of polynomial ${\displaystyle v(x)}$ when divided by the generator polynomial ${\displaystyle g(x)}$ i.e.

${\displaystyle S(x)\equiv v(x)\equiv (a(x)g(x)+e(x))\equiv e(x)\mod g(x)}$ as ${\displaystyle (a(x)g(x))\equiv 0\mod g(x)}$.

For correcting two errors

Let the field elements ${\displaystyle X_{1}}$ and ${\displaystyle X_{2}}$ be the two error location numbers. If only one error occurs then ${\displaystyle X_{2}}$ is equal to zero and if none occurs both are zero.

Let ${\displaystyle S_{1}={v}(\alpha )}$ and ${\displaystyle S_{3}={v}(\alpha ^{3})}$.

These field elements are called "syndromes". Now because ${\displaystyle g(x)}$ is zero at primitive elements ${\displaystyle \alpha }$ and ${\displaystyle \alpha ^{3}}$, so we can write ${\displaystyle S_{1}=e(\alpha )}$ and ${\displaystyle S_{3}=e(\alpha ^{3})}$. If say two errors occur, then

${\displaystyle S_{1}=\alpha ^{i}+\alpha ^{i'}}$ and ${\displaystyle S_{3}=\alpha ^{3i}+\alpha ^{3i'}}$.

And these two can be considered as two pair of equations in ${\displaystyle GF(2^{m})}$ with two unknowns and hence we can write

${\displaystyle S_{1}=X_{1}+X_{2}}$ and ${\displaystyle S_{3}=(X_{1})^{3}+(X_{2})^{3}}$.

Hence if the two pair of nonlinear equations can be solved cyclic codes can used to correct two errors.

Hamming code

The Hamming(7,4) code may be written as a cyclic code over GF(2) with generator ${\displaystyle 1+x+x^{3}}$. In fact, any binary Hamming code of the form Ham(r, 2) is equivalent to a cyclic code, and any Hamming code of the form Ham(r,q) with r and q-1 relatively prime is also equivalent to a cyclic code. Given a Hamming code of the form Ham(r,2) with ${\displaystyle r\geq 3}$, the set of even codewords forms a cyclic ${\displaystyle }$-code.

Hamming code for correcting single errors

A code whose minimum distance is at least 3, have a check matrix all of whose columns are distinct and non zero. If a check matrix for a binary code has ${\displaystyle m}$ rows, then each column is an ${\displaystyle m}$-bit binary number. There are ${\displaystyle 2^{m}-1}$ possible columns. Therefore, if a check matrix of a binary code with ${\displaystyle d_{min}}$ at least 3 has ${\displaystyle m}$ rows, then it can only have ${\displaystyle 2^{m}-1}$ columns, not more than that. This defines a ${\displaystyle (2^{m}-1,2^{m}-1-m)}$ code, called Hamming code.

It is easy to define Hamming codes for large alphabets of size ${\displaystyle q}$. We need to define one ${\displaystyle H}$ matrix with linearly independent columns. For any word of size ${\displaystyle q}$ there will be columns who are multiples of each other. So, to get linear independence all non zero ${\displaystyle m}$-tuples with one as a top most non zero element will be chosen as columns. Then two columns will never be linearly dependent because three columns could be linearly dependent with the minimum distance of the code as 3.

So, there are ${\displaystyle (q^{m}-1)/(q-1)}$ nonzero columns with one as top most non zero element. Therefore, a Hamming code is a ${\displaystyle }$ code.

Now, for cyclic codes, Let ${\displaystyle \alpha }$ be primitive element in ${\displaystyle GF(q^{m})}$, and let ${\displaystyle \beta =\alpha ^{q-1}}$. Then ${\displaystyle \beta ^{(q^{m}-1)/(q-1)}=1}$ and thus ${\displaystyle \beta }$ is a zero of the polynomial ${\displaystyle x^{(q^{m}-1)/(q-1)}-1}$ and is a generator polynomial for the cyclic code of block length ${\displaystyle n=(q^{m}-1)/(q-1)}$.

But for ${\displaystyle q=2}$, ${\displaystyle \alpha =\beta }$. And the received word is a polynomial of degree ${\displaystyle n-1}$ given as

${\displaystyle v(x)=a(x)g(x)+e(x)}$

where, ${\displaystyle e(x)=0}$ or ${\displaystyle x^{i}}$ where ${\displaystyle i}$ represents the error locations.

But we can also use ${\displaystyle \alpha ^{i}}$ as an element of ${\displaystyle GF(2^{m})}$ to index error location. Because ${\displaystyle g(\alpha )=0}$, we have ${\displaystyle v(\alpha )=\alpha ^{i}}$ and all powers of ${\displaystyle \alpha }$ from ${\displaystyle 0}$ to ${\displaystyle 2^{m}-2}$ are distinct. Therefore, we can easily determine error location ${\displaystyle i}$ from ${\displaystyle \alpha ^{i}}$ unless ${\displaystyle v(\alpha )=0}$ which represents no error. So, a Hamming code is a single error correcting code over ${\displaystyle GF(2)}$ with ${\displaystyle n=2^{m}-1}$ and ${\displaystyle k=n-m}$.

For correcting burst errors

From Hamming distance concept, a code with minimum distance ${\displaystyle 2t+1}$ can correct any ${\displaystyle t}$ errors. But in many channels error pattern is not very arbitrary, it occurs within very short segment of the message. Such kind of errors are called burst errors. So, for correcting such errors we will get a more efficient code of higher rate because of the less constraints. Cyclic codes are used for correcting burst error. In fact, cyclic codes can also correct cyclic burst errors along with burst errors. Cyclic burst errors are defined as

A cyclic burst of length ${\displaystyle t}$ is a vector whose nonzero components are among ${\displaystyle t}$ (cyclically) consecutive components, the first and the last of which are nonzero.

In polynomial form cyclic burst of length ${\displaystyle t}$ can be described as ${\displaystyle e(x)=x^{i}b(x)\mod (x^{n}-1)}$ with ${\displaystyle b(x)}$ as a polynomial of degree ${\displaystyle t-1}$ with nonzero coefficient ${\displaystyle b_{0}}$. Here ${\displaystyle b(x)}$ defines the pattern and ${\displaystyle x^{i}}$ defines the starting point of error. Length of the pattern is given by deg${\displaystyle b(x)+1}$. The syndrome polynomial is unique for each pattern and is given by

${\displaystyle s(x)=e(x)\mod g(x)}$

A linear block code that corrects all burst errors of length ${\displaystyle t}$ or less must have at least ${\displaystyle 2t}$ check symbols. Proof: Because any linear code that can correct burst pattern of length ${\displaystyle t}$ or less cannot have a burst of length ${\displaystyle 2t}$ or less as a codeword because if it did then a burst of length ${\displaystyle t}$ could change the codeword to burst pattern of length ${\displaystyle t}$, which also could be obtained by making a burst error of length ${\displaystyle t}$ in all zero codeword. Now, any two vectors that are non zero in the first ${\displaystyle 2t}$ components must be from different co-sets of an array to avoid their difference being a codeword of bursts of length ${\displaystyle 2t}$. Therefore, number of such co-sets are equal to number of such vectors which are ${\displaystyle q^{2t}}$. Hence at least ${\displaystyle q^{2t}}$ co-sets and hence at least ${\displaystyle 2t}$ check symbol.

This property is also known as Rieger bound and it is similar to the Singleton bound for random error correcting.

Fire codes as cyclic bounds

In 1959, Philip Fire presented a construction of cyclic codes generated by a product of a binomial and a primitive polynomial. The binomial has the form ${\displaystyle x^{c}+1}$ for some positive odd integer ${\displaystyle c}$. Fire code is a cyclic burst error correcting code over ${\displaystyle GF(q)}$ with the generator polynomial

${\displaystyle g(x)=(x^{2t-1}-1)p(x)}$

where ${\displaystyle p(x)}$ is a prime polynomial with degree ${\displaystyle m}$ not smaller than ${\displaystyle t}$ and ${\displaystyle p(x)}$ does not divide ${\displaystyle x^{2t-1}-1}$. Block length of the fire code is the smallest integer ${\displaystyle n}$ such that ${\displaystyle g(x)}$ divides ${\displaystyle x^{n}-1}$.

A fire code can correct all burst errors of length t or less if no two bursts ${\displaystyle b(x)}$ and ${\displaystyle x^{j}b'(x)}$ appear in the same co-set. This can be proved by contradiction. Suppose there are two distinct nonzero bursts ${\displaystyle b(x)}$ and ${\displaystyle x^{j}b'(x)}$ of length ${\displaystyle t}$ or less and are in the same co-set of the code. So, their difference is a codeword. As the difference is a multiple of ${\displaystyle g(x)}$ it is also a multiple of ${\displaystyle x^{2t-1}-1}$. Therefore,

${\displaystyle b(x)=x^{j}b'(x)\mod (x^{2t-1}-1)}$.

This shows that ${\displaystyle j}$ is a multiple of ${\displaystyle 2t-1}$, So

${\displaystyle b(x)=x^{l(2t-1)}b'(x)}$

for some ${\displaystyle l}$. Now, as ${\displaystyle l(2t-1)}$ is less than ${\displaystyle t}$ and ${\displaystyle l}$ is less than ${\displaystyle q^{m}-1}$ so ${\displaystyle (x^{l(2t-1)}-1)b(x)}$ is a codeword. Therefore,

${\displaystyle (x^{l(2t-1)}-1)b(x)=a(x)(x^{2t-1}-1)p(x)}$.

Since ${\displaystyle b(x)}$ degree is less than degree of ${\displaystyle p(x)}$,${\displaystyle p(x)}$ cannot divide ${\displaystyle b(x)}$. If ${\displaystyle l}$ is not zero, then ${\displaystyle p(x)}$ also cannot divide ${\displaystyle x^{l(2t-1)}-1}$ as ${\displaystyle l}$ is less than ${\displaystyle q^{m}-1}$ and by definition of ${\displaystyle m}$, ${\displaystyle p(x)}$ divides ${\displaystyle x^{l(2t-1)}-1}$ for no ${\displaystyle l}$ smaller than ${\displaystyle q^{m}-1}$. Therefore ${\displaystyle l}$ and ${\displaystyle j}$ equals to zero. That means both that both the bursts are same, contrary to assumption.

Fire codes are the best single burst correcting codes with high rate and they are constructed analytically. They are of very high rate and when ${\displaystyle m}$ and ${\displaystyle t}$ are equal, redundancy is least and is equal to ${\displaystyle 3t-1}$. By using multiple fire codes longer burst errors can also be corrected.

For error detection cyclic codes are widely used and are called ${\displaystyle t-1}$ cyclic redundancy codes.

On Fourier transform

Applications of Fourier transform are widespread in signal processing. But their applications are not limited to the complex fields only; Fourier transforms also exist in the Galois field ${\displaystyle GF(q)}$. Cyclic codes using Fourier transform can be described in a setting closer to the signal processing.

Fourier transform over finite fields

Fourier transform over finite fields

The discrete Fourier transform of vector ${\displaystyle v=v_{0},v_{1},....,v_{n-1}}$ is given by a vector ${\displaystyle V=V_{0},V_{1},.....,V_{n-1}}$ where,

${\displaystyle V_{k}}$ = ${\displaystyle \Sigma _{i=0}^{n-1}e^{-j2\pi n^{-1}ik}v_{i}}$ where,

${\displaystyle k=0,.....,n-1}$

where exp(${\displaystyle -j2\pi /n}$) is an ${\displaystyle n}$th root of unity. Similarly in the finite field ${\displaystyle n}$th root of unity is element ${\displaystyle \omega }$ of order ${\displaystyle n}$. Therefore

If ${\displaystyle v=(v_{0},v_{1},....,v_{n-1})}$ is a vector over ${\displaystyle GF(q)}$, and ${\displaystyle \omega }$ be an element of ${\displaystyle GF(q)}$ of order ${\displaystyle n}$, then Fourier transform of the vector ${\displaystyle v}$ is the vector ${\displaystyle V=(V_{0},V_{1},.....,V_{n-1})}$ and components are given by

${\displaystyle V_{j}}$ = ${\displaystyle \Sigma _{i=0}^{n-1}\omega ^{ij}v_{i}}$ where,

${\displaystyle k=0,.....,n-1}$

Here ${\displaystyle i}$ is time index, ${\displaystyle j}$ is frequency and ${\displaystyle V}$ is the spectrum. One important difference between Fourier transform in complex field and Galois field is that complex field ${\displaystyle \omega }$ exists for every value of ${\displaystyle n}$ while in Galois field ${\displaystyle \omega }$ exists only if ${\displaystyle n}$ divides ${\displaystyle q-1}$. In case of extension fields, there will be a Fourier transform in the extension field ${\displaystyle GF(q^{m})}$ if ${\displaystyle n}$ divides ${\displaystyle q^{m}-1}$ for some ${\displaystyle m}$. In Galois field time domain vector ${\displaystyle v}$ is over the field ${\displaystyle GF(q)}$ but the spectrum ${\displaystyle V}$ may be over the extension field ${\displaystyle GF(q^{m})}$.

Spectral description

Any codeword of cyclic code of blocklength ${\displaystyle n}$ can be represented by a polynomial ${\displaystyle c(x)}$ of degree at most ${\displaystyle n-1}$. Its encoder can be written as ${\displaystyle c(x)=a(x)g(x)}$. Therefore, in frequency domain encoder can be written as ${\displaystyle C_{j}=A_{j}G_{j}}$. Here codeword spectrum ${\displaystyle C_{j}}$ has a value in ${\displaystyle GF(q^{m})}$ but all the components in the time domain are from ${\displaystyle GF(q)}$. As the data spectrum ${\displaystyle A_{j}}$ is arbitrary, the role of ${\displaystyle G_{j}}$ is to specify those ${\displaystyle j}$ where ${\displaystyle C_{j}}$ will be zero.

Thus, cyclic codes can also be defined as

Given a set of spectral indices, ${\displaystyle A=(j_{1},....,j_{n-k})}$, whose elements are called check frequencies, the cyclic code ${\displaystyle C}$ is the set of words over ${\displaystyle GF(q)}$ whose spectrum is zero in the components indexed by ${\displaystyle j_{1},...,j_{n-k}}$. Any such spectrum ${\displaystyle C}$ will have components of the form ${\displaystyle A_{j}G_{j}}$.

So, cyclic codes are vectors in the field ${\displaystyle GF(q)}$ and the spectrum given by its inverse fourier transform is over the field ${\displaystyle GF(q^{m})}$ and are constrained to be zero at certain components. But every spectrum in the field ${\displaystyle GF(q^{m})}$ and zero at certain components may not have inverse transforms with components in the field ${\displaystyle GF(q)}$. Such spectrum can not be used as cyclic codes.

Following are the few bounds on the spectrum of cyclic codes.

BCH bound

If ${\displaystyle n}$ be a factor of ${\displaystyle (q^{m}-1)}$ for some ${\displaystyle m}$. The only vector in ${\displaystyle GF(q)^{n}}$ of weight ${\displaystyle d-1}$ or less that has ${\displaystyle d-1}$ consecutive components of its spectrum equal to zero is all-zero vector.

Hartmann-Tzeng bound

If ${\displaystyle n}$ be a factor of ${\displaystyle (q^{m}-1)}$ for some ${\displaystyle m}$, and ${\displaystyle b}$ an integer that is coprime with ${\displaystyle n}$. The only vector ${\displaystyle v}$ in ${\displaystyle GF(q)^{n}}$ of weight ${\displaystyle d-1}$ or less whose spectral components ${\displaystyle V_{j}}$ equal zero for ${\displaystyle j=\ell _{1}+\ell _{2}b(\mod n)}$, where ${\displaystyle \ell _{1}=0,....,d-s-1}$ and ${\displaystyle \ell _{2}=0,....,s-1}$, is the all zero vector.

Roos bound

If ${\displaystyle n}$ be a factor of ${\displaystyle q^{m}-1}$ for some ${\displaystyle m}$ and ${\displaystyle GCD(n,b)=1}$. The only vector in ${\displaystyle GF(q)^{n}}$ of weight ${\displaystyle d-1}$ or less whose spectral components ${\displaystyle V_{j}}$ equal to zero for ${\displaystyle j=l_{1}+l_{2}b(\mod n)}$, where ${\displaystyle l_{1}=0,...,d-s-2}$ and ${\displaystyle l_{2}}$ takes at least ${\displaystyle s+1}$ values in the range ${\displaystyle 0,....,d-2}$, is the all-zero vector.

Quadratic residue codes

When the prime ${\displaystyle l}$ is a quadratic residue modulo the prime ${\displaystyle p}$ there is a quadratic residue code which is a cyclic code of length ${\displaystyle p}$, dimension ${\displaystyle (p+1)/2}$ and minimum weight at least ${\displaystyle {\sqrt {p}}}$ over ${\displaystyle GF(l)}$.

Generalizations

A constacyclic code is a linear code with the property that for some constant λ if (c₁,c₂,...,c_n) is a codeword then so is (λc_n,c₁,...,c_n-1). A negacyclic code is a constacyclic code with λ=-1. A quasi-cyclic code has the property that for some s, any cyclic shift of a codeword by s places is again a codeword. A double circulant code is a quasi-cyclic code of even length with s=2. Quasi-twisted codes and multi-twisted codes are further generalizations of constacyclic codes.

See also

• BCH code
• Binary Golay code
• Cyclic redundancy check
• Eugene Prange
• Reed–Muller code
• Ternary Golay code

Notes

1. Van Lint 1998, p. 76
2. Van Lint 1998, p. 80
3. Hill 1988, pp. 159–160
4. Blahut 2003, Theorem 5.5.1
5. Hill 1988, pp. 162–163
6. P. Fire, E, P. (1959). A class of multiple-error-correcting binary codes for non-independent errors. Sylvania Reconnaissance Systems Laboratory, Mountain View, CA, Rept. RSL-E-2, 1959.
7. Wei Zhou, Shu Lin, Khaled Abdel-Ghaffar. Burst or random error correction based on Fire and BCH codes. ITA 2014: 1-5 2013.
8. Van Lint 1998, p. 75
9. ^ MacWilliams & Sloane 1977, p. 506
10. Aydin, Nuh; Siap, Irfan; K. Ray-Chaudhuri, Dijen (2001). "The Structure of 1-Generator Quasi-Twisted Codes and New Linear Codes". Designs, Codes and Cryptography. 24 (3): 313–326. doi:10.1023/A:1011283523000. S2CID 17376783.
11. Aydin, Nuh; Halilović, Ajdin (2017). "A generalization of quasi-twisted codes: multi-twisted codes". Finite Fields and Their Applications. 45: 96–106. arXiv:1701.01044. doi:10.1016/j.ffa.2016.12.002. S2CID 7694655.

References

• Blahut, Richard E. (2003), Algebraic Codes for Data Transmission (2nd ed.), Cambridge University Press, ISBN 0-521-55374-1
• Hill, Raymond (1988), A First Course In Coding Theory, Oxford University Press, ISBN 0-19-853803-0
• MacWilliams, F. J.; Sloane, N. J. A. (1977), The Theory of Error-Correcting Codes, New York: North-Holland Publishing, ISBN 0-444-85011-2
• Van Lint, J. H. (1998), Introduction to Coding Theory, Graduate Texts in Mathematics 86 (3rd ed.), Springer Verlag, ISBN 3-540-64133-5

Further reading

• Ranjan Bose, Information theory, coding and cryptography, ISBN 0-07-048297-7
• Irving S. Reed and Xuemin Chen, Error-Control Coding for Data Networks, Boston: Kluwer Academic Publishers, 1999, ISBN 0-7923-8528-4.
• Scott A. Vanstone, Paul C. Van Oorschot, An introduction to error correcting codes with applications, ISBN 0-7923-9017-2

External links

• John Gill's (Stanford) class notes – Notes #3, October 8, Handout #9 Archived 2012-10-23 at the Wayback Machine, EE 387.
• Jonathan Hall's (MSU) class notes – Chapter 8. Cyclic codes - pp. 100 - 123
• David Terr. "Cyclic Code". MathWorld.

This article incorporates material from cyclic code on PlanetMath, which is licensed under the Creative Commons Attribution/Share-Alike License.

• Coding theory
• Finite fields