Clipping Can Improve the Performance of Spatially Coupled Sparse Superposition Codes

Liang, Shansuo; Ma, Junjie; Li, Ping

doi:10.1109/lcomm.2017.2754262

Cited by 17 publications

(13 citation statements)

References 18 publications

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“…The clipping ratio (CR) of clip(•) in ( 4) is defined as CR = 10log 10 ( 2 /E{z 2 }). Then, the codeword c is given by a normalized clipping function defined as [6]: where α…”

Section: B Regularly Clipped Sr Codementioning

confidence: 99%

“…Spatialcoupling, originally introduced for LDPC codes, was used in SR codes [4] to obtain a Bayes-optimal decoding performance for any compression rate. Recently, it was found that regular clipping, a technique that is generally used to reduce the peakto-average-power ratio (PAPR), can also improve the section error rate (SER) performance of SR codes significantly [6]. When a signal is clipped, we compensate by scaling the clipped signal to maintain a fixed transmit power; the intuition of clipped SR codes is that this scaling is more beneficial than the penalty of distortion due to clipping.…”

Section: Introductionmentioning

confidence: 99%

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Irregularly Clipped Sparse Regression Codes

Liu

Kurkoski

2021

2021 IEEE Information Theory Workshop (ITW)

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Recently, it was found that clipping can significantly improve the section error rate (SER) performance of sparse regression (SR) codes if an optimal clipping threshold is chosen. In this paper, we propose irregularly clipped SR codes, where multiple clipping thresholds are applied to symbols according to a distribution, to further improve the SER performance of SR codes. Orthogonal approximate message passing (OAMP) algorithm is used for decoding. Using state evolution, the distribution of irregular clipping thresholds is optimized to minimize the SER of OAMP decoding. As a result, optimized irregularly clipped SR codes achieve a better tradeoff between clipping distortion and noise distortion than regularly clipped SR codes. Numerical results demonstrate that irregularly clipped SR codes achieve 0.4 dB gain in signal-to-noise-ratio (SNR) over regularly clipped SR codes at code length ≈ 2.5 × 10 4 and SER ≈ 10 −5 . We further show that irregularly clipped SR codes are robust over a wide range of code rates.

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“…The clipping ratio (CR) of clip(•) in ( 4) is defined as CR = 10log 10 ( 2 /E{z 2 }). Then, the codeword c is given by a normalized clipping function defined as [6]: where α…”

Section: B Regularly Clipped Sr Codementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Irregularly Clipped Sparse Regression Codes

Liu

Kurkoski

2021

2021 IEEE Information Theory Workshop (ITW)

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“…5.1 has parameters ω = 3 and Λ = 7. This base matrix construction was also used in [79] for SC-SPARCs. Other base matrix constructions can be found in [72,35,8,12].…”

Section: Spatially Coupled Sparc Constructionmentioning

confidence: 99%

Sparse Regression Codes

Venkataramanan

Tatikonda

Barron

2019

FNT in Communications and Information Theory

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Developing computationally-efficient codes that approach the Shannon-theoretic limits for communication and compression has long been one of the major goals of information and coding theory. There have been significant advances towards this goal in the last couple of decades, with the emergence of turbo codes, sparse-graph codes, and polar codes. These codes are designed primarily for discrete-alphabet channels and sources. For Gaussian channels and sources, where the alphabet is inherently continuous, Sparse Superposition Codes or Sparse Regression Codes (SPARCs) are a promising class of codes for achieving the Shannon limits.This monograph provides a unified and comprehensive over-view of sparse regression codes, covering theory, algorithms, and practical implementation aspects. The first part of the monograph focuses on SPARCs for AWGN channel coding, and the second part on SPARCs for lossy compression (with squared error distortion criterion). In the third part, SPARCs are used to construct codes for Gaussian multi-terminal channel and source coding models such as broadcast channels, multiple-access channels, and source and channel coding with side information. The monograph concludes with a discussion of open problems and directions for future work. v vi Chapter 1 A: β: 0, c 2 , 0, c L , 0, , 0 0, M columns M columns M columns Section 1 Section 2 Section L T n rows 0, c 1 , 0, 0, Figure 1.1: A Gaussian sparse regression codebook of block length n: A is a design matrix with independent Gaussian entries, and β is a sparse vector with one non-zero in each of L sections. Codewords are of the form Aβ, i.e., linear combinations of the columns corresponding to the non-zeros in β. The message is indexed by the locations of the non-zeros, and the values c 1 , . . . , c L are fixed a priori.

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“…There are L L C sections in β C(c) , with the non-zero entry in each section being equal to 1; we denote by β C(c) the th of these sections, for ∈ [L/L C ]. Then, (16) implies…”

Section: Performance Of the Amp Decodermentioning

confidence: 99%

“…This base matrix construction was also used in[16] for SC-SPARCs. Other base matrix constructions can be found in[7],[10],[13],[17].…”

mentioning

confidence: 99%

Spatially Coupled Sparse Regression Codes: Design and State Evolution Analysis

Hsieh

Rush

Venkataramanan

2018

2018 IEEE International Symposium on Information Theory (ISIT)

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We consider the design and analysis of spatially coupled sparse regression codes (SC-SPARCs), which were recently introduced by Barbier et al. for efficient communication over the additive white Gaussian noise channel. SC-SPARCs can be efficiently decoded using an Approximate Message Passing (AMP) decoder, whose performance in each iteration can be predicted via a set of equations called state evolution. In this paper, we give an asymptotic characterization of the state evolution equations for SC-SPARCs. For any given base matrix (that defines the coupling structure of the SC-SPARC) and rate, this characterization can be used to predict whether AMP decoding will succeed in the large system limit. We then consider a simple base matrix defined by two parameters (ω, Λ), and show that AMP decoding succeeds in the large system limit for all rates R < C. The asymptotic result also indicates how the parameters of the base matrix affect the decoding progression. Simulation results are presented to evaluate the performance of SC-SPARCs defined with the proposed base matrix.

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Clipping Can Improve the Performance of Spatially Coupled Sparse Superposition Codes

Cited by 17 publications

References 18 publications

Irregularly Clipped Sparse Regression Codes

Irregularly Clipped Sparse Regression Codes

Sparse Regression Codes

Spatially Coupled Sparse Regression Codes: Design and State Evolution Analysis

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