BEAST decoding - asymptotic complexity

Summary, in English

BEAST is a bidirectional efficient algorithm for searching trees that performs soft-decision maximum-likelihood (ML) decoding of block codes. The decoding complexity of BEAST is significantly reduced compared to the Viterbi algorithm. An analysis of the asymptotic BEAST decoding complexity verifies BEAST's high efficiency compared to other algorithms. The best of the obtained asymptotic upper bounds on the BEAST decoding complexity is better than previously known bounds for ML decoding in a wide range of code rates.

• Författare: Irina Bocharova; Rolf Johannesson; Boris Kudryashov
• Avdelning/ar: Institutionen för elektro- och informationsteknik
• Publiceringsår: 2005
• Språk: Engelska
• Publikation/Tidskrift/Serie: 2005 IEEE Information Theory Workshop
• Länkar: http://dx.doi.org/10.1109/ITW.2005.1531846
• Dokumenttyp: Konferensbidrag
• Ämne: Electrical Engineering, Electronic Engineering, Information Engineering
• Nyckelord: maximum likelihood decoding; tree searching; block codes; decision trees; computational complexity
• Conference name: IEEE IT SOC Information Theory Workshop 2005 on Coding and Complexity
• Conference date: 2005-08-29 - 2005-09-01
• Conference place: Rotorua, New Zealand
• Status: Published
• ISBN/ISSN/Övrigt: ISBN: 0-7803-9480-1