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Hardware Architecture for Guessing Random Additive Noise Decoding Markov Order (GRAND-MO).

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Název: Hardware Architecture for Guessing Random Additive Noise Decoding Markov Order (GRAND-MO).
Autoři: Abbas, Syed Mohsin, Jalaleddine, Marwan, Gross, Warren J.
Zdroj: Journal of Signal Processing Systems for Signal, Image & Video Technology; Oct2022, Vol. 94 Issue 10, p1047-1065, 19p
Abstrakt: Communication channels with memory are often sensitive to burst noise, which drastically reduces the decoding performance of standard channel code decoders, and this degradation worsens as channel memory increases. Hence, interleavers and de-interleavers are usually used to reduce the effects of burst noise at the expense of increased latency in the communication system. The delay imposed by interleavers/de-interleavers and the performance deterioration induced by channel memory are unacceptable in novel applications that require ultra-low latency and high decoding performance. Guessing Random Additive Noise Decoding (GRAND) is a universal Maximum Likelihood (ML) decoding technique for short-length and high-rate channel codes. GRAND Markov Order (GRAND-MO) is a hard-input variant of GRAND that has been specifically developed for communication channels with memory that are subject to burst noise. GRAND-MO can be used directly on hard demodulated channel signals, removing the requirement for extra interleavers/de-interleavers and considerably reducing overall latency in communication systems. This paper describes a high-throughput GRAND-MO VLSI architecture that can achieve an average throughput of up to 52 Gbps and 64 Gbps for code lengths of 128 and 79, respectively. Furthermore, we propose improvements to the GRAND-MO algorithm to simplify hardware implementation and reduce decoding complexity. When compared to GRANDAB, a hard-input variant of GRAND, the proposed improved GRAND-MO algorithm yields a decoding performance gain of 2 ∼ 3 dB at a target FER of 10 - 5 . Similarly, as compared to the (79, 64) BCH code decoder, the proposed GRAND-MO decoder has a 33% reduced worst-case latency and a 2 dB gain in decoding performance at a target FER of 10 - 5 . [ABSTRACT FROM AUTHOR]
Copyright of Journal of Signal Processing Systems for Signal, Image & Video Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Hardware Architecture for Guessing Random Additive Noise Decoding Markov Order (GRAND-MO).
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  Data: <searchLink fieldCode="AR" term="%22Abbas%2C+Syed+Mohsin%22">Abbas, Syed Mohsin</searchLink><br /><searchLink fieldCode="AR" term="%22Jalaleddine%2C+Marwan%22">Jalaleddine, Marwan</searchLink><br /><searchLink fieldCode="AR" term="%22Gross%2C+Warren+J%2E%22">Gross, Warren J.</searchLink>
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  Data: Journal of Signal Processing Systems for Signal, Image & Video Technology; Oct2022, Vol. 94 Issue 10, p1047-1065, 19p
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Communication channels with memory are often sensitive to burst noise, which drastically reduces the decoding performance of standard channel code decoders, and this degradation worsens as channel memory increases. Hence, interleavers and de-interleavers are usually used to reduce the effects of burst noise at the expense of increased latency in the communication system. The delay imposed by interleavers/de-interleavers and the performance deterioration induced by channel memory are unacceptable in novel applications that require ultra-low latency and high decoding performance. Guessing Random Additive Noise Decoding (GRAND) is a universal Maximum Likelihood (ML) decoding technique for short-length and high-rate channel codes. GRAND Markov Order (GRAND-MO) is a hard-input variant of GRAND that has been specifically developed for communication channels with memory that are subject to burst noise. GRAND-MO can be used directly on hard demodulated channel signals, removing the requirement for extra interleavers/de-interleavers and considerably reducing overall latency in communication systems. This paper describes a high-throughput GRAND-MO VLSI architecture that can achieve an average throughput of up to 52 Gbps and 64 Gbps for code lengths of 128 and 79, respectively. Furthermore, we propose improvements to the GRAND-MO algorithm to simplify hardware implementation and reduce decoding complexity. When compared to GRANDAB, a hard-input variant of GRAND, the proposed improved GRAND-MO algorithm yields a decoding performance gain of 2 ∼ 3 dB at a target FER of 10 - 5 . Similarly, as compared to the (79, 64) BCH code decoder, the proposed GRAND-MO decoder has a 33% reduced worst-case latency and a 2 dB gain in decoding performance at a target FER of 10 - 5 . [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Signal Processing Systems for Signal, Image & Video Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Oct2022
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