Linear hash-functions and their applications to error detection and correction

TL;DR

This paper introduces linear hash functions for error detection and correction, demonstrating their effectiveness and simplicity, especially in detecting limited errors with performance near theoretical bounds.

Contribution

It presents a new approach using linear hash functions for error correction that outperforms some existing methods in simplicity and reliability.

Findings

Performance slightly exceeds Varshamov-Gilbert bound

Proposed codes are simpler and more flexible than existing ones

Can detect errors with certainty up to a certain limit

Abstract

We describe and explore so-called linear hash functions and show how they can be used to build error detection and correction codes. The method can be applied for different types of errors (for example, burst errors). When the method is applied to a model where number of distorted letters is limited, the obtained estimate of its performance is slightly better than the known Varshamov-Gilbert bound. We also describe random code whose performance is close to the same boundary, but its construction is much simpler. In some cases the obtained methods are simpler and more flexible than the known ones. In particular, the complexity of the obtained error detection code and the well-known CRC code is close, but the proposed code, unlike CRC, can detect with certainty errors whose number does not exceed a predetermined limit.