Compressed Coding, AMP-Based Decoding, and Analog Spatial Coupling

Liang, Shansuo; Liang, Chulong; Ma, Junjie; Li, Ping

doi:10.1109/tcomm.2020.3024871

Cited by 13 publications

(7 citation statements)

References 41 publications

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“…Paralleling the development of AMP for sparse regression codes [24] and drawing inspiration from concatenated AMP systems [33], [34], we wish to create a composite iterative process to recover state vector s from y that exploits the sparsity in s and the parity structure of the LDPC code. This can be accomplished by incorporating message passing on the factor graph of the LDPC code into the AMP denoiser.…”

Section: B Sparse Regression Ldpc Decodingmentioning

confidence: 99%

On Sparse Regression LDPC Codes

R.¹,

Jean-Francois²,

Narayanan³

2023

Preprint

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Belief propagation applied to iterative decoding and sparse recovery through approximate message passing (AMP) are two research areas that have seen monumental progress in recent decades. Inspired by these advances, this article introduces sparse regression LDPC codes and their decoding. Sparse regression codes (SPARCs) are a class of error correcting codes that build on ideas from compressed sensing and can be decoded using AMP. In certain settings, SPARCs are known to achieve capacity; yet, their performance suffers at finite block lengths. Likewise, LDPC codes can be decoded efficiently using belief propagation and can also be capacity achieving. This article introduces a novel concatenated coding structure that combines an LDPC outer code with a SPARC-inspired inner code. Efficient decoding for such a code can be achieved using AMP with a denoiser that performs belief propagation on the factor graph of the outer LDPC code. The proposed framework exhibits performance improvements over SPARCs and standard LDPC codes for finite block lengths and results in a steep waterfall in error performance, a phenomenon not observed in uncoded SPARCs. Findings are supported by numerical results.

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Section: B Sparse Regression Ldpc Decodingmentioning

confidence: 99%

On Sparse Regression LDPC Codes

R.¹,

Jean-Francois²,

Narayanan³

2023

Preprint

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“…Concatenated schemes that combine AMP with an outer code have been proposed in [20], [27] to improve the finite block length performance in the context of single user SPARC. In [20], a high rate LDPC code is used to protect sections of AMP with less allocated power.…”

Section: )mentioning

confidence: 99%

“…In other words, the LDPC decoder does not assist the convergence of AMP and it only works on soft outputs produced by AMP upon convergence. Compressed-coding is proposed and analyzed in [27], where AMP is combined with an outer generic forward error correction (FEC) code. Therein, the authors show that with a careful design of the underlying FEC code, the compressed-coding scheme can achieve the single user Gaussian capacity asymptotically.…”

Section: )mentioning

confidence: 99%

“…In this paper, we do not rely on such assumptions; rather, we prove that state evolution for AMP is indeed accurate under certain conditions for the proposed architecture. It is also worth noting that the schemes in [20], [27] do not admit straightforward extensions to the unsourced random access paradigm, which points to the novel and unique character of our contributions.…”

Section: )mentioning

confidence: 99%

“…It is worth mentioning that the denoiser in Definition 6 reduces to the PME with uninformative prior probabilities introduced in [5] when the number of composite steps performed on the factor graph of the tree code is zero. Next, DRAFT we turn to the divergence of Dη PME t (r), which gives rise to the Onsager correction term found in (27). Although…”

Section: B Dynamic Pme Denoisingmentioning

confidence: 99%

See 2 more Smart Citations

Unsourced Random Access with Coded Compressed Sensing: Integrating AMP and Belief Propagation

Amalladinne¹,

Pradhan²,

Rush³

et al. 2020

Preprint

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Sparse regression codes with approximate message passing (AMP) decoding have gained much attention in recent times. The concepts underlying this coding scheme extend to unsourced random access with coded compressed sensing (CCS), as first demonstrated by Fengler, Jung, and Caire. Specifically, their approach employs a concatenated coding framework with an inner AMP decoder followed by an outer tree decoder. In their original implementation, these two components work independently of each other, with the tree decoder acting on the static output of the AMP decoder. This article introduces a novel framework where the inner AMP decoder and the outer tree decoder operate in tandem, dynamically passing information back and forth to take full advantage of the underlying CCS structure. This scheme necessitates the redesign of the tree code as to enable belief propagation in a computationally tractable manner. The enhanced architecture exhibits significant performance benefits over a range of system parameters. The error performance of the proposed scheme can be accurately predicted through a set of equations, known as state evolution of AMP. These findings are supported both analytically and through numerical methods.

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Compressed-Coding and Analog Spatial-Coupling using AMP based Decoding

Liang

et al. 2020

GLOBECOM 2020 - 2020 IEEE Global Communications Conference

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Compressed Coding, AMP-Based Decoding, and Analog Spatial Coupling

Cited by 13 publications

References 41 publications

On Sparse Regression LDPC Codes

On Sparse Regression LDPC Codes

Unsourced Random Access with Coded Compressed Sensing: Integrating AMP and Belief Propagation

Compressed-Coding and Analog Spatial-Coupling using AMP based Decoding

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