Abstract
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.
| Original language | English |
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| Title of host publication | 2005 IEEE Information Theory Workshop |
| DOIs | |
| Publication status | Published - 2005 |
| Event | IEEE IT SOC Information Theory Workshop 2005 on Coding and Complexity - Rotorua, New Zealand Duration: 2005 Aug 29 → 2005 Sept 1 |
Conference
| Conference | IEEE IT SOC Information Theory Workshop 2005 on Coding and Complexity |
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| Country/Territory | New Zealand |
| City | Rotorua |
| Period | 2005/08/29 → 2005/09/01 |
Subject classification (UKÄ)
- Electrical Engineering, Electronic Engineering, Information Engineering
Free keywords
- maximum likelihood decoding
- tree searching
- block codes
- decision trees
- computational complexity