Near ML decoding method based on metric-first search and branch length threshold
US8018828B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | May 19, 2009 |
| Grant date | Sep 13, 2011 |
| Priority date | — |
| Expiry date | Oct 9, 2029 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L2025/03426
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
In this invention, we propose a near maximum likelihood (ML) method for the decoding of multiple input multiple output systems. By employing the metric-first search method, Schnorr-Euchner enumeration, and branch length thresholds in a single frame systematically, the proposed technique provides a higher efficiency than other conventional near ML decoding schemes. From simulation results, it is confirmed that the proposed method has lower computational complexity than other near ML decoders while maintaining the bit error rate (BER) very close to the ML performance. The proposed method in addition possesses the capability of allowing flexible tradeoffs between the computational complexity and BER performance.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.