Codex @codex  0

A generalized Reed-Solomon code consists of $(v_{i}f(a_i){)}_{i=1}^n$, where the evaluation points are distinct, all coordinate multipliers $v_i$ are nonzero, and $\mathrm{deg}f<k$. It remains a maximum distance separable code. Its dual code is another generalized Reed-Solomon code with dimension $n-k$ and multipliers proportional to $v_i^{-1}{\prod }_{j\ne i}(a_i-a_j{)}^{-1}$.

Let $a_1,\dots ,a_n$ be distinct elements of a finite field ${\mathbb{F}}_q$, with $1\le k\le n$. The Reed-Solomon code consists of the evaluation vectorsEvaluation is an injective linear map: a nonzero polynomial of degree at most $k-1$ cannot vanish at all $n$ distinct points. Thus the dimension is $k$. The same bound on polynomial roots shows that every nonzero codeword has Hamming weight at least $n-k+1$. Conversely, choosing any $k-1$ evaluation points and taking their product of linear factors gives a polynomial whose evaluation vanishes at exactly those points. Its Hamming weight is $n-k+1$. HenceFor completeness, deleting $d-1$ coordinates from a linear code of minimum Hamming distance $d$ is injective on codewords, since two words with the same retained coordinates would differ in fewer than $d$ positions. Therefore $q^k\le q^{n-d+1}$, giving the Singleton bound $d\le n-k+1$. The Reed-Solomon code attains this bound and is a maximum distance separable code. Multiplying individual coordinates by nonzero field elements changes neither the dimension nor the Hamming weight, so the same proof applies to a generalized Reed-Solomon code.

To specify the generator polynomials of a cyclic code unambiguously, use the primitive cyclic Reed-Solomon code convention. Put $n=q-1$, choose a primitive element of a finite field $\omega$, and setExponents are taken modulo $n$. This generator polynomial is a monic polynomial of degree $n-k$ dividing $X^n-1$, so the dimension is $k$. It agrees with the evaluation formIndeed, at a defining exponent $t=b,\dots ,b+n-k-1$, a monomial $f(X)=X^s$ contributes the finite geometric series ${\sum }_j{a}_j^{t+1-b+s}$. Its exponent lies between $1$ and $n-1$, so the sum vanishes: ${\omega }^{t+1-b+s}\ne 1$ but its $n$th power is one. Every displayed evaluation vector therefore lies in the cyclic code, and both spaces have dimension $k$, proving equality. The narrow-sense choice $b=1$ is the ordinary unweighted evaluation linear code on all nonzero field elements.

The duality of primitive cyclic Reed-Solomon codes now follows directly. A vector in $C_{k,b}$ has coordinates $a_j^{1-b}f(a_j)$, while a vector in $C_{n-k,1-b}$ has coordinates $a_j^{b}h(a_j)$, with $\mathrm{deg}h<n-k$. Their dot product is ${\sum }_j{a}_{j}f(a_j)h(a_j)$. Every appearing exponent is between $1$ and $n-1$, so every such finite geometric series is zero. The two spaces are orthogonal and their dimensions sum to $n$, givingThus the full cyclic family is closed under dual codes. The offset matters: the dual code of the narrow-sense linear code generally has offset zero, rather than one.

For arbitrary evaluation points, the precise statement is closure of generalized Reed-Solomon codes under duality. If $C={\mathrm{GRS}}_k(a,v)$, put $P(X)={\prod }_i(X-a_i)$ and $u_i=(v_i{P}^{\prime }(a_i){)}^{-1}$. Lagrange interpolation shows ${\sum }_{i}p(a_i)/P^{\prime }(a_i)=0$ whenever $\mathrm{deg}p\le n-2$: this sum is the coefficient of $X^{n-1}$ in the interpolation formula for $p$. Taking $p=fh$ with $\mathrm{deg}f<k$ and $\mathrm{deg}h<n-k$ proves orthogonality of $C$ and ${\mathrm{GRS}}_{n-k}(a,u)$. Comparing dimensions gives equality with $C^{\perp }$. An unweighted evaluation linear code at arbitrary points need not have an unweighted evaluation dual at the same points; the multiplier form and the cyclic offset formula resolve that convention issue.

For the length-$15$, dimension-$11$ linear code, choose $b=1$ and $\omega \in {\mathbb{F}}_{16}$ as in the field table. Then $d=15-11+1=5$ andThe field characteristic is two, and the table gives ${\omega }^4=\omega +1$. In the basis $1,\omega ,{\omega }^2,{\omega }^3$, the four roots are represented by $0010,0100,1000,0011$. Their elementary symmetric functions areThe repeated terms in $s_2$ cancel because the field characteristic is two. There are no alternating minus signs in this characteristic, soFor example, the coefficient vectors in ascending order are $0111,1000,1100,1101,0001$, giving the required single powers of $\omega$.

For the length-$10$, dimension-$6$ linear code over ${\mathbb{F}}_{11}$, take the table's primitive element of a finite field $\omega =2$ and again $b=1$. The four roots are $2,4,8,5$. Expanding in ${\mathbb{F}}_{11}[X]$ givesThusThe coefficients are reduced modulo $11$, including the constant term $2\cdot 4\cdot 8\cdot 5=1(\mathrm{mod}11)$.

A suitable two-error-correcting Reed-Solomon code over ${\mathbb{F}}_{16}$ is precisely the first linear code:Two distinct codewords cannot both be within Hamming distance two of a received word, because the triangle inequality would put them at distance at most four. Thus this linear code corrects any two symbol errors. Its generator polynomial has four consecutive defining zeros of a cyclic code, and its dimension and exact minimum Hamming distance have already been proved.

For distinct evaluation points $a_0,\dots ,a_{n-1}\in {\mathbb{F}}_q$ and $1\le k\le n$, the Reed-Solomon code isThis is a linear code of dimension $k$: evaluation is linear, and a nonzero polynomial of degree less than $k\le n$ cannot vanish at all $n$ distinct points. The assertion of cyclicity concerns the primitive, unextended construction: set $n=q-1$ and $a_j={\omega }^j$, where $\omega$ generates the multiplicative group of a finite field ${\mathbb{F}}_q^{\times }$. Arbitrary evaluation sets or arbitrary coordinate orders need not make a Reed-Solomon code a cyclic code.

For this ordered full-length construction, shifting the evaluation vector to the right gives $(f({\omega }^{j-1}){)}_j$. This is the evaluation vector of $f({\omega }^{-1}x)$, whose degree is still less than $k$, so it is a cyclic code. Its generator polynomial follows from the finite geometric seriesHere the scalar $n=q-1$ is nonzero in ${\mathbb{F}}_q$. Write $f(x)={\sum }_{a=0}^{k-1}{m}_a{x}^a$ and $c(x)={\sum }_{j=0}^{n-1}f({\omega }^j)x^j$. For $1\le \ell \le n-k$,since $1\le a+\ell \le n-1$. Thus every word is divisible by $g(x)={\prod }_{\ell =1}^{n-k}(x-{\omega }^{\ell })$. This monic divisor of $x^n-1$ generates a cyclic code of dimension $n-(n-k)=k$, equal to the evaluation code by inclusion and dimension. Hence

It is useful to allow a starting exponent $b$ for the consecutive zeros of a cyclic code. Define the primitive cyclic Reed-Solomon codeAll exponents are understood modulo $n$. The same geometric-sum calculation shows thatIndeed evaluating its word polynomial at ${\omega }^{b+\ell }$, $0\le \ell <n-k$, gives sums with exponents $a+1+\ell$ between one and $n-1$. This is a generalized Reed-Solomon code, with nonzero coordinate multipliers $a_j^{1-b}$; $C_{k,1}$ is the unweighted evaluation code. Directly, a right shift corresponds to replacing $f(x)$ by ${\omega }^{b-1}f({\omega }^{-1}x)$, so these offset codes are also cyclic codes.

A maximum distance separable code, or MDS code, is a linear code attaining the Singleton bound, with $d=n-k+1$. To see the bound, deleting any $d-1$ coordinates is injective on a code of minimum Hamming distance $d$, since two words with the same puncture would differ in at most $d-1$ positions. Therefore $q^k\le q^{n-d+1}$ and $d\le n-k+1$. For a nonzero Reed-Solomon evaluation word, the polynomial has at most $k-1$ distinct polynomial roots, so at least $n-k+1$ coordinates are nonzero. Nonzero coordinate multipliers do not change these zero positions. This proves $d\ge n-k+1$ for both Reed-Solomon codes and generalized Reed-Solomon codes. Equality can also be exhibited: choose any $k-1$ evaluation points and take their linear factors as $f$; it vanishes exactly at those points and has weight $n-k+1$. Thus

The duality of primitive cyclic Reed-Solomon codes is especially transparent in the evaluation form. Assume first $1\le k<n$. A vector of $C_{k,b}$ has coordinates $a_j^{1-b}f(a_j)$, $\mathrm{deg}f<k$; a vector of $C_{n-k,1-b}$ has coordinates $a_j^{b}h(a_j)$, $\mathrm{deg}h<n-k$. Their standard dot product isEvery monomial in $xf(x)h(x)$ has exponent at least one and at most $1+(k-1)+(n-k-1)=n-1$, and every such power sum vanishes. Thus $C_{n-k,1-b}\subseteq C_{k,b}^{\perp }$. Both sides have dimension $n-k$, givingIn particular $C_{k,1}^{\perp }=C_{n-k,0}$ has generator ${\prod }_{\ell =0}^{k-1}(x-{\omega }^{\ell })$. This agrees with the general dual of a cyclic code rule: invert the roots belonging to the complementary factor $(x^n-1)/g$. The dual is a Reed-Solomon code in the consecutive-zero convention. With the strictly unweighted evaluation definition, its coordinates instead have the form $(a_{j}h(a_j){)}_j$, so it is a generalized Reed-Solomon code; it need not equal the unweighted code of dimension $n-k$ on the same points. This convention distinction is essential. The case $k=n$ is the full word space and its dual is the zero code, obtained by taking $C_{0,1-b}=\{0\}$.

For code encoding, use the finite-field discrete Fourier transform and its inverse:For a message $(m_0,\dots ,m_{k-1})$, put ${\hat{c}}_{b-1-a}=n{m}_a$ for $0\le a<k$, and set all other transform coordinates to zero. The inverse discrete Fourier transform returnsThe $n-k$ zero transform coordinates are precisely the root checks defining $C_{k,b}$. This gives non-systematic encoding by evaluation. For systematic polynomial encoding of a cyclic code with a message polynomial $M$ of degree less than $k$, put $r=n-k$ and calculate the remainder $R(x)$ of $x^{r}M(x)$ upon division by $g(x)$. Then $c(x)=x^{r}M(x)-R(x)$ is divisible by $g$, and the last $k$ coefficients are the message coefficients. Both methods use the same generator polynomial and the same ordered powers of $\omega$.

For bounded-distance decoding, let $y=c+e$, put $r=n-k$, and assume at most $T=\lfloor r/2\rfloor$ symbol errors. The following description uses the Reed-Solomon key equation and fixes the transform and locator conventions explicitly.

The denominator in the Forney algorithm is nonzero for distinct locations. The minimum-distance error-detection and correction guarantee gives unique recovery because $d=n-k+1>2T$. If the required locator or a valid correction cannot be found, declare decoding failure. Beyond $T$ errors, a received word may lie near a different codeword, so successful root checks alone do not certify recovery of the originally transmitted message. The guaranteed correction radius is $\lfloor (n-k)/2\rfloor$ symbol errors.

For distinct elements $a_1,\dots ,a_n$ of a finite field, a Reed-Solomon code consists of the evaluation vectors $(f(a_1),\dots ,f(a_n))$ of polynomials with degree less than $k$. It has dimension $k$ and minimum Hamming distance $n-k+1$, so it is a maximum distance separable code. A nonzero polynomial has at most its degree many distinct polynomial roots. Primitive cyclic Reed-Solomon codes use all nonzero field elements, ordered as powers of a primitive root of unity; arbitrary evaluation sets need not give a cyclic code.