Analog Joint Source Channel Coding Using Space-Filling Curves and MMSE Decoding

Hu, Yichuan; Garcia-Frías, Javier; Lamarca, M.

doi:10.1109/dcc.2009.45

Cited by 16 publications

(24 citation statements)

References 13 publications

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“…Three values of k, k = 1, k = 2 and k = 4, are considered, and the applied non-linear mappings are the standard 2:1 and 4:1 space-filling curves in (2) and (3), respectively, plus the circular approximation. By applying the parameter optimization method proposed in [11], ∆ and α are jointly optimized for each channel signalto-noise ratio (CSNR), and optimal energy allocation is performed for each band. In order to reduce the optimization complexity, in all results presented here ML rather than MMSE is considered, which leads to some performance loss.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Moreover, since no long blocks are used the delay is very small. We have recently shown that, by properly optimizing the curve parameters, the use of MMSE decoding for Gaussian and Laplacian sources transmitted over AWGN channels results in a performance that is very close to the theoretical limits in the whole SNR region [11].…”

Section: Ii-b Space-filling Curves As Analog Joint-source Channel Enmentioning

confidence: 95%

“…We saw in [11] that by properly optimizing the curve parameters the resulting performance is excellent for both Gaussian and Laplacian sources. Specifically, for the whole CSNR region the gap to the theoretical limits is below 1 dB in the case of Gaussian sources, and below 1.4 dB for Laplacian sources.…”

Section: Ii-b Space-filling Curves As Analog Joint-source Channel Enmentioning

confidence: 97%

“…non-redundant measurements at the output of the CS block have to be optimally encoded into the M transmitted samples. Interestingly, it is well known in the literature that this can be achieved with less complexity and delay than digital systems by utilizing space-filling curves [9], [10], [11]. We will briefly review these ideas in Section II.B.…”

Section: Proposed Systemmentioning

confidence: 99%

See 3 more Smart Citations

Non-linear mappings for transmission of compressed sensing images

Iglesias

Lü

Garcia-Frías

et al. 2010

2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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We extend a recently proposed system based on the combination of compressive sensing and non-linear processing for the transmission of images over noisy channels. The key idea is the use of very simple non-linear mappings that act as analog joint source-channel encoders, processing the compressive sensing measurements proceeding from an analog source and producing continuous amplitude samples that are transmitted directly through the noisy channel. In order to reduce complexity a very simple approximation for the non-linear mapping is introduced. The proposed framework clearly outperforms systems based on stand-alone compressive sensing with almost negligible complexity.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Ii-b Space-filling Curves As Analog Joint-source Channel Enmentioning

confidence: 95%

Section: Ii-b Space-filling Curves As Analog Joint-source Channel Enmentioning

confidence: 97%

Section: Proposed Systemmentioning

confidence: 99%

See 2 more Smart Citations

Non-linear mappings for transmission of compressed sensing images

Iglesias

Lü

Garcia-Frías

et al. 2010

2010 48th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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show abstract

“…MMSE decoding has been shown to achieve a substantial performance improvement over ML decoding at low CSNRs under 2:1 bandwidth reduction [10]. For 1:2 bandwidth expansion, the MMSE decoding rule can be written as followŝ…”

Section: A Shannon-kotel'nikov Mappingmentioning

confidence: 99%

Hybrid digital-analog source-channel coding with one-to-three bandwidth expansion

Saleh

Alajaji

Chan

2011

2011 12th Canadian Workshop on Information Theory

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Abstract-We consider the problem of bandwidth expansion for lossy joint source-channel coding over a memoryless Gaussian channel. A low delay 1:3 bandwidth expansion hybrid digitalanalog coding system, which combines a scalar quantizer and a 1:2 nonlinear analog coder, is proposed. It is shown that our proposed system outperforms the 1:3 generalized hybrid scalar quantizer linear coder in terms of signal-to-distortion ratio (SDR). A lower bound on the system SDR is also derived.

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Non-linear coding for improved performance in compressive sensing

Wang

Garcia-Frías

et al. 2009

2009 43rd Annual Conference on Information Sciences and Systems

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We propose a system based on the combination of compressive sensing and non-linear processing that shows excellent robustness against noise. The key idea is the use of nonlinear mappings that act as analog joint source-channel encoders, processing the compressive sensing measurements proceeding from an analog source and producing continuous amplitude samples that are transmitted directly through the noisy channel. As we will show in our simulation results, the proposed framework is readily applicable in practical systems such as imaging, and clearly outperforms systems based on stand-alone compressive sensing.

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Analog Joint Source Channel Coding Using Space-Filling Curves and MMSE Decoding

Abstract: We investigate the performance of a discrete-time all-analog-processing

Cited by 16 publications

References 13 publications

Non-linear mappings for transmission of compressed sensing images

Non-linear mappings for transmission of compressed sensing images

Hybrid digital-analog source-channel coding with one-to-three bandwidth expansion

Non-linear coding for improved performance in compressive sensing

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