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Upper Bounds for the Performance of Turbo-Like Codes and Low Density Parity Check Codes

机译:Turbo-Like码和低密度奇偶校验码性能的上限

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Researchers have investigated many upper bound techniques applicable to error probabilities on the maximum likelihood (ML) decoding performance of turbo-like codes and low density parity check (LDPC) codes in recent years for a long codeword block size. This is because it is trivial for a short codeword block size. Previous research efforts, such as the simple bound technique [20] recently proposed, developed upper bounds for LDPC codes and turbo-like codes using ensemble codes or the uniformly interleaved assumption. This assumption bounds the performance averaged over all ensemble codes or all interleavers. Another previous research effort [21] obtained the upper bound of turbo-like code with a particular interleaver using a truncated union bound which requires information of the minimum Hamming distance and the number of codewords with the minimum Hamming distance. However, it gives the reliable bound only in the region of the error floor where the minimum Hamming distance is dominant, i.e., in the region of high signal-to-noise ratios. Therefore, currently an upper bound on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix cannot be calculated because of heavy complexity so that only average bounds for ensemble codes can be obtained using a uniform interleaver assumption. In this paper, we propose a new bound technique on ML decoding performance for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix using ML estimated weight distributions and we also show that the practical iterative decoding performance is approximately suboptimal in ML sense because the simulation performance of iterative decoding is worse than the proposed upper bound and no wonder, even worse than ML decoding performance. In order to show this point, we compare the simulation results with the proposed upper bound and previous bounds. The proposed bound technique is based on the simple bound with an approximate weight distribution including several exact smallest distance terms, not with the ensemble distribution or the uniform interleaver assumption. This technique also shows a tighter upper bound than any other previous bound techniques for turbo-like code with a particular interleaver and LDPC code with a particular parity check matrix.
机译:近年来,研究人员针对长码字块大小,研究了许多适用于Turbo式码和低密度奇偶校验(LDPC)码的最大似然(ML)解码性能的错误概率的上限技术。这是因为对于短码字块大小而言,这是微不足道的。先前的研究工作,例如最近提出的简单绑定技术[20],使用集成代码或均匀交织的假设为LDPC码和类似Turbo的代码开发了上限。该假设限制了所有集成代码或所有交织器的平均性能。另一项先前的研究成果[21]使用截短的并集边界,使用特定的交织器获得了类turbo编码的上限,这要求最小汉明距离信息和具有最小汉明距离的代码字数量。然而,它仅在最小汉明距离占主导的误差基底的区域中,即在高信噪比的区域中,才给出可靠的界限。因此,由于复杂度高,目前无法计算具有特定交织器的turbo样码和具有特定奇偶校验矩阵的LDPC码的ML解码性能的上限,因此使用统一的交织器只能获得整体码的平均边界假设。在本文中,我们针对具有特定交织器的turbo式码和具有特定奇偶校验矩阵的LDPC码,使用ML估计的权重分布提出了一种新的ML解码性能绑定技术,并且我们还表明,实际的迭代解码性能近似于次优从ML的意义上讲,因为迭代解码的仿真性能比拟议的上限差,而且也难怪甚至比ML解码性能差。为了说明这一点,我们将仿真结果与建议的上限和先前的上限进行了比较。所提出的绑定技术基于具有近似权重分布(包括几个确切的最小距离项)的简单绑定,而不具有合奏分布或均匀交织器假设。与具有特定交织器的turbo式代码和具有特定奇偶校验矩阵的LDPC码相比,该技术还显示出比其他任何先前绑定技术更严格的上限。

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