High-Throughput VLSI Architecture for GRAND Markov Order

Syed Mohsin Abbas, Marwan Jalaleddine, W. Gross
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引用次数: 9

Abstract

Guessing Random Additive Noise Decoding (GRAND) is a recently proposed Maximum Likelihood (ML) decoding technique. Irrespective of the structure of the error correcting code, GRAND tries to guess the noise that corrupted the codeword in order to decode any linear error-correcting block code. GRAND Markov Order (GRAND-MO) is a variant of GRAND that is useful to decode error correcting code transmitted over communication channels with memory which are vulnerable to burst noise. Usually, interleavers and de-interleavers are used in communication systems to mitigate the effects of channel memory. Interleaving and de-interleaving introduce undesirable latency, which increases with channel memory. To prevent this added latency penalty, GRAND-MO can be directly used on the hard demodulated channel signals. This work reports the first GRAND-MO hardware architecture which achieves an average throughput of up to 52 Gbps and 64 Gbps for a code length of 128 and 79 respectively. Compared to the GRANDAB, hard-input variant of GRAND, the proposed architecture achieves 3 dB gain in decoding performance for a target FER of 10−5. Similarly, comparing the GRAND-MO decoder with a decoder tailored for a (79,64) BCH code showed that the proposed architecture achieves 33% higher worst case throughput and 2 dB gain in decoding performance.
大马尔可夫阶的高吞吐量VLSI架构
猜测随机加性噪声解码(GRAND)是最近提出的一种极大似然解码技术。无论纠错码的结构如何,GRAND都会尝试猜测损坏码字的噪声,以便解码任何线性纠错块码。广义马尔可夫阶(GRAND Markov Order, GRAND- mo)是广义马尔可夫阶(GRAND- mo)的一种变体,可用于在具有存储的易受突发噪声影响的通信信道上传输的纠错码的译码。通常,在通信系统中使用交织器和去交织器来减轻信道存储器的影响。交错和反交错会引入不必要的延迟,延迟会随着通道内存的增加而增加。为了防止这种额外的延迟损失,grandmo可以直接用于硬解调信道信号。这项工作报告了第一个GRAND-MO硬件架构,该架构分别在代码长度为128和79时实现了高达52 Gbps和64 Gbps的平均吞吐量。与GRAND的硬输入变体GRANDAB相比,该架构在目标FER为10−5的情况下实现了3 dB的解码性能增益。同样,将GRAND-MO解码器与为(79,64)BCH码量身定制的解码器进行比较表明,所提出的架构在最坏情况下的吞吐量提高了33%,解码性能提高了2 dB。
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