Data subset selection algorithm for reducing data-pattern autocorrelations
US7876866B1 · kind B1 · utility
Assignee
Inventors
Key dates
| Filing date | Jan 26, 2006 |
| Grant date | Jan 25, 2011 |
| Priority date | — |
| Expiry date | Aug 28, 2029 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L7/0337
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
A method and apparatus are provided for reducing, and preferably substantially eliminating, data-pattern autocorrelations found in digital communication systems. The method employed is referred to as Data Subset Selection (DSS) and is implemented in the form of DSS engine. Autocorrelations in the data-pattern can cause many digital adaptive systems to converge to an incorrect solution. For example, the LMS method, which is often used in adaptive filtering applications, can converge to an incorrect set of filter coefficients in the presence of data-pattern autocorrelations. Digital timing recovery methods are also susceptible. Other impairments that result from data-pattern autocorrelations include increased convergence time and increased steady-state chatter. DSS reduces, and preferably substantially eliminates, data-pattern autocorrelations by selecting a subset of the data stream that either demonstrates smaller autocorrelations or no autocorrelations, thus improving the performance of the aforementioned digital adaptive systems.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.