Guessing Random Additive Noise Decoding With Symbol Reliability Information (SRGRAND)

Duffy, Ken R.; Médard, Muriel; An, Wei

doi:10.1109/tcomm.2021.3114315

Cited by 39 publications

(28 citation statements)

References 90 publications

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“…In this section, we analyze the proposed joint decryptiondecoding scheme as presented in Section IV using GRAND [7]. GRAND algorithms operate by sequentially inverting putative noise effects from the demodulated received sequence and querying if what remains is in the code-book [7], [21]- [23]. If those noise effects are queried in order from mostlikely to least likely, the first instance where a code-book member is found is an ML decoding [7].…”

Section: Decrypting Encoded Datamentioning

confidence: 99%

Partial Encryption after Encoding for Security and Reliability in Data Systems

Cohen¹,

D’Oliveira²,

Duffy³

et al. 2022

Preprint

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We consider the problem of secure and reliable communication over a noisy multipath network. Previous work considering a noiseless version of our problem proposed a hybrid universal network coding cryptosystem (HUNCC). By combining an information-theoretically secure encoder together with partial encryption, HUNCC is able to obtain security guarantees, even in the presence of an all-observing eavesdropper. In this paper, we propose a version of HUNCC for noisy channels (N-HUNCC). This modification requires four main novelties. First, we present a network coding construction which is jointly, individually secure and error-correcting. Second, we introduce a new security definition which is a computational analogue of individual security, which we call individual indistinguishability under chosen ciphertext attack (individual IND-CCA1), and show that N-HUNCC satisfies it. Third, we present a noise based decoder for N-HUNCC, which permits the decoding of the encoded-thenencrypted data. Finally, we discuss how to select parameters for N-HUNCC and its error-correcting capabilities.

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Section: Decrypting Encoded Datamentioning

confidence: 99%

Partial Encryption after Encoding for Security and Reliability in Data Systems

Cohen¹,

D’Oliveira²,

Duffy³

et al. 2022

Preprint

Self Cite

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Section: Introduction Shannon's Pioneering Workmentioning

confidence: 99%

“…Symbol Reliability GRAND (SRGRAND) [38], [39] is a variant that avails of the most limited quantized soft information where one additional bit tags each demodulated symbol as being reliably or unreliably received. SRGRAND retains the desirable parallelizability of the original algorithm, is readily implementable in hardware, and provides a 0.5 − 0.75 dB gain over hard-detection GRAND [39]. At the other extreme, Soft GRAND (SGRAND) [40] is a variant that uses real-valued soft information per demodulated bit to build a dedicated noiseeffect query order for each received signal.…”

Section: Introduction Shannon's Pioneering Workmentioning

confidence: 99%

Ordered Reliability Bits Guessing Random Additive Noise Decoding

Duffy¹,

An²,

Médard³

2022

Preprint

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Error correction techniques traditionally focus on the co-design of restricted code-structures in tandem with codespecific decoders that are computationally efficient when decoding long codes in hardware. Modern applications are, however, driving demand for ultra-reliable low-latency communications (URLLC), rekindling interest in the performance of shorter, higher-rate error correcting codes, and raising the possibility of revisiting universal, code-agnostic decoders.To that end, here we introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Ordered Reliability Bits GRAND that can accurately decode any moderate redundancy block-code. It is designed with efficient circuit implementation in mind, and determines accurate decodings while retaining the original hard detection GRAND algorithm's suitability for a highly parallelized implementation in hardware.ORBGRAND is shown to provide excellent soft decision block error performance for codes of distinct classes (BCH, CA-Polar and RLC) with modest complexity, while providing better block error rate performance than CA-SCL, a state of the art soft detection CA-Polar decoder. ORBGRAND offers the possibility of an accurate, energy efficient soft detection decoder suitable for delivering URLLC in a single hardware realization.

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“…A natural question is how to make use of soft detection information, when it is available, in order to improve GRAND's query order and several proposals have been made. Symbol Reliability GRAND (SRGRAND) [24], [25] avails of the most limited quantized soft information where one additional bit tags each demodulated symbol as being reliably or unreliably received. SRGRAND is mathematically analysable, implementable in hardware, and provides a 0.5 − 0.75 dB gain over hard-detection GRAND [25].…”

Section: Introductionmentioning

confidence: 99%

“…Symbol Reliability GRAND (SRGRAND) [24], [25] avails of the most limited quantized soft information where one additional bit tags each demodulated symbol as being reliably or unreliably received. SRGRAND is mathematically analysable, implementable in hardware, and provides a 0.5 − 0.75 dB gain over hard-detection GRAND [25]. Soft GRAND (SGRAND) [26] uses real-valued soft information per demodulated bit to build a bespoke noise-effect query order for each received signal.…”

Section: Introductionmentioning

confidence: 99%

Quantized Guessing Random Additive Noise Decoding

Duffy¹,

Gabhart²,

Yuan³

et al. 2022

Preprint

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We introduce a soft-detection variant of Guessing Random Additive Noise Decoding (GRAND) called Quantized GRAND (QGRAND) that can efficiently decode any moderate redundancy block-code in an algorithm that is suitable for highly parallelized implementation in hardware. QGRAND can avail of any level of quantized soft information, and is shown to provide near maximum likelihood decoding performance when provided with five or more bits of soft information per received bit.

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Guessing Random Additive Noise Decoding With Symbol Reliability Information (SRGRAND)

Cited by 39 publications

References 90 publications

Partial Encryption after Encoding for Security and Reliability in Data Systems

Partial Encryption after Encoding for Security and Reliability in Data Systems

Ordered Reliability Bits Guessing Random Additive Noise Decoding

Quantized Guessing Random Additive Noise Decoding

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