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arXiv cs/0601023cs.ITmath.IT
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基于咬尾网格的高效收敛最大似然译码

Efficient Convergent Maximum Likelihood Decoding on Tail-Biting Trellises

Priti Shankar, P. N. A. Kumar, K. Sasidharan, B. S. Rajan, A. S. Madhu

摘要:

提出了一种基于咬尾网格的精确最大似然(ML)译码算法,该算法具有很好的平均情况性能。提出了一个近似的变体,其模拟性能被观察到几乎是无法区分的确切的一个在所有值的信号噪声比,并有效地执行计算相当于最多两轮咬尾网格。分析了近似算法,推导出其输出与ML输出不同的条件。本文给出了在AWGN信道上对(24,1/2)扩展Golay码的16状态咬尾格形和存储量分别为4和6的两种码率1/2卷积码的咬尾格形的精确和近似算法的仿真结果。我们的算法的一个优点是,它们不受极限环的影响或伪码字的存在。

英文摘要:

An algorithm for exact maximum likelihood(ML) decoding on tail-biting trellises is presented, which exhibits very good average case behavior. An approximate variant is proposed, whose simulated performance is observed to be virtually indistinguishable from the exact one at all values of signal to noise ratio, and which effectively performs computations equivalent to at most two rounds on the tail-biting trellis. The approximate algorithm is analyzed, and the conditions under which its output is different from the ML output are deduced. The results of simulations on an AWGN channel for the exact and approximate algorithms on the 16 state tail-biting trellis for the (24,12) Extended Golay Code, and tail-biting trellises for two rate 1/2 convolutional codes with memories of 4 and 6 respectively, are reported. An advantage of our algorithms is that they do not suffer from the effects of limit cycles or the presence of pseudocodewords.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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