Generalized Inverse Based Decoding
This provides a foundational algebraic framework for decoding in coding theory, potentially improving error correction in communications, though it appears incremental as it builds on known ISD methods.
The paper tackles the syndrome decoding problem and low weight codeword problem by introducing Generalized Inverse based Decoding (GID), an algebraic framework that generalizes existing information set decoding algorithms and allows searching the entire solution space efficiently, with experimental results showing it reaches easy weights in very few iterations.
The concept of Generalized Inverse based Decoding (GID) is introduced, as an algebraic framework for the syndrome decoding problem (SDP) and low weight codeword problem (LWP). The framework has ground on two characterizations by generalized inverses (GIs), one for the null space of a matrix and the other for the solution space of a system of linear equations over a finite field. Generic GID solvers are proposed for SDP and LWP. It is shown that information set decoding (ISD) algorithms, such as Prange, Lee-Brickell, Leon, and Stern's algorithms, are particular cases of GID solvers. All of them search GIs or elements of the null space under various specific strategies. However, as the paper shows the ISD variants do not search through the entire space, while our solvers do even when they use just one Gaussian elimination. Apart from these, our GID framework clearly shows how each ISD algorithm, except for Prange's solution, can be used as an SDP or LWP solver. A tight reduction from our problems, viewed as optimization problems, to the MIN-SAT problem is also provided. Experimental results show a very good behavior of the GID solvers. The domain of easy weights can be reached by a very few iterations and even enlarged.