2020 54th Annual Conference on Information Sciences and Systems (CISS) 2020
DOI: 10.1109/ciss48834.2020.1570617412
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5G NR CA-Polar Maximum Likelihood Decoding by GRAND

Abstract: CA-Polar codes have been selected for all control channel communications in 5G NR, but accurate, computationally feasible decoders are still subject to development. Here we report the performance of a recently proposed class of optimally precise Maximum Likelihood (ML) decoders, GRAND, that can be used with any block-code. As published theoretical results indicate that GRAND is computationally efficient for shortlength, high-rate codes and 5G CA-Polar codes are in that class, here we consider GRAND's utility f… Show more

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Cited by 33 publications
(20 citation statements)
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“…Doing so, however, requires that additional information be passed from the receiver to the decoder, which typically necessitates its quantization for efficient transmission. Symbol Reliability GRAND (SRGRAND) [28], [29], [30] uses the most extreme quantized soft information where one additional bit tags each demodulated symbol as being reliably or unreliably received. Implementing SRGRAND in hardware retains the desirable parallelizability and is no more challenging than doing so for GRAND, but symbol reliability information does not exploit the full benefit of soft information.…”
Section: Introductionmentioning
confidence: 99%
“…Doing so, however, requires that additional information be passed from the receiver to the decoder, which typically necessitates its quantization for efficient transmission. Symbol Reliability GRAND (SRGRAND) [28], [29], [30] uses the most extreme quantized soft information where one additional bit tags each demodulated symbol as being reliably or unreliably received. Implementing SRGRAND in hardware retains the desirable parallelizability and is no more challenging than doing so for GRAND, but symbol reliability information does not exploit the full benefit of soft information.…”
Section: Introductionmentioning
confidence: 99%
“…The original design established mathematical properties of a class of Maximum Likelihood hard-detection decoders [14], [15], but omitted important practical implementation details that we complete here for Markovian channels (to be described in section II-B). For soft detection with increasing levels of quantization and memoryless channels, a series of variants have been proposed: SRGRAND [19], [20], ORBGRAND [21] and SGRAND [22]. In the current work, we adapt GRAND to make it suitable for channels that induce Markov correlated error bursts in a hard-detection setting, establishing that significant gains in Block Error Rate (BLER) performance are possible for short, high-rate code, obviating the need to use interleaving and thus enabling URLLC.…”
Section: Introductionmentioning
confidence: 99%
“…It is well known that taking into account soft values improves decoding performance. Symbol reliability GRAND (SRGRAND) characterizes the received symbols into two categories: reliable or unreliable according to a threshold depending on the channel conditions [11,12]. Compared to GRAND, better decoding accuracy is achieved with reduced complexity.…”
Section: Guessing Random Additive Noise Decodingmentioning
confidence: 99%