| ### C.8.1 Codes and the decoding problem #### Codes Let be a field with elements. A linear code is a linear subspace of endowed with the Hamming metric. Hamming distance between x,y . Hamming weight of x . Minimum distance of the code . The code of dimension and minimum distance is denoted as . A matrix whose rows are the base vectors of is the generator matrix. A matrix with the property is the check matrix. #### Cyclic codes The code is cyclic, if for every codeword in its cyclic shift is again a codeword in . When working with cyclic codes, vectors are usually presented as polynomials. So is represented by the polynomial with , more precisely is an element of the factor ring . Cyclic codes over of length correspond one-to-one to ideals in this factor ring. We assume for cyclic codes that . Let be the splitting field of over . Then has a primitive -th root of unity which will be denoted by . A cyclic code is uniquely given by a defining set which is a subset of such that A cyclic code has several defining sets. #### Decoding problem Complete decoding: Given and a code , so that is at distance from the code, find . Bounded up to half the minimum distance: With the additional assumption , a codeword with the above property is unique. #### Decoding via systems solving One distinguishes between two concepts: Generic decoding: Solve some system and obtain some "closed" formulas . Evaluating these formulas at data specific to a received word should yield a solution to the decoding problem. For example for . The roots of yield error positions, see the section on the general error-locator polynomial. Online decoding: Solve some system . The solutions should solve the decoding problem. #### Computational effort Generic decoding. Here, preprocessing is very hard, whereas decoding is relatively simple (if the formulas are sparse). Online decoding. In this case, decoding is the hard part.

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