4 research outputs found
Analysis of Joint Source Channel LDPC Coding for Correlated Sources Transmission over Noisy Channels
In this paper, a Joint Source Channel coding scheme
based on LDPC codes is investigated. We consider two concatenated
LDPC codes, one allows to compress a correlated source and the
second to protect it against channel degradations. The original
information can be reconstructed at the receiver by a joint decoder,
where the source decoder and the channel decoder run in parallel by
transferring extrinsic information. We investigate the performance of
the JSC LDPC code in terms of Bit-Error Rate (BER) in the case
of transmission over an Additive White Gaussian Noise (AWGN)
channel, and for different source and channel rate parameters.
We emphasize how JSC LDPC presents a performance tradeoff
depending on the channel state and on the source correlation. We
show that, the JSC LDPC is an efficient solution for a relatively
low Signal-to-Noise Ratio (SNR) channel, especially with highly
correlated sources. Finally, a source-channel rate optimization has
to be applied to guarantee the best JSC LDPC system performance
for a given channel
Exploiting 2-Dimensional Source Correlation in Channel Decoding with Parameter Estimation
Traditionally, it is assumed that source coding is perfect and therefore, the redundancy of the source encoded bit-stream is zero. However, in reality, this is not the case as the existing source encoders are imperfect and yield residual redundancy at the output. The residual redundancy can be exploited by using Joint Source Channel Coding (JSCC) with Markov chain as the source. In several studies, the statistical knowledge of the sources has been assumed to be perfectly available at the receiver. Although the result was better in terms of the BER performance, practically, the source correlation knowledge were not always available at the receiver and thus, this could affect the reliability of the outcome. The source correlation on all rows and columns of the 2D sources were well exploited by using a modified Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm in the decoder. A parameter estimation technique was used jointly with the decoder to estimate the source correlation knowledge. Hence, this research aims to investigate the parameter estimation for 2D JSCC system which reflects a practical scenario where the source correlation knowledge are not always available. We compare the performance of the proposed joint decoding and estimation technique with the ideal 2D JSCC system with perfect knowledge of the source correlation knowledge. Simulation results reveal that our proposed coding scheme performs very close to the ideal 2D JSCC system
Joint Source Channel Decoding Exploiting 2D Source Correlation with Parameter Estimation for Image Transmission over Rayleigh Fading Channels
This paper investigates the performance of a 2- Dimensional (2D) Joint Source Channel Coding (JSCC) system assisted with parameter estimation for 2D image transmission over an Additive White Gaussian Noise (AWGN) channel and a Rayleigh fading channel. Baum-Welsh Algorithm (BWA)  is employed in the proposed 2D JSCC system to estimate the source correlation statistics during channel decoding. The source correlation is then exploited during channel decoding using a Modified Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. The performance of the 2D JSCC system with the BWA-based parameter estimation technique (2D-JSCC-PET1) is evaluated via image transmission simulations. Two images, each exhibits strong and weak source correlation are considered in the evaluation by measuring the Peak Signal Noise Ratio of the decoded images at the receiver. The proposed 2D-JSCC-PET1 system is compared with various benchmark systems. Simulation results reveal that the 2D-JSCC-PET1 system outperforms the other benchmark systems (performance gain of 4.23 dB over the 2D-JSCC-PET2 system and 6.10 dB over the 2D JSCC system). The proposed system also can perform very close to the ideal 2D JSCC system relying on the assumption of perfect source correlation knowledge at the receiver that shown only 0.88 dB difference in performance gain
Exploitation of 2D binary source correlation using turbo block codes with fine-tuning
This article proposes a joint source-channel coding technique for two-dimensional (2D) binary Markov sources by using concatenated turbo block codes composed of two Bose, Chaudhuri, Hocquenghem (BCH) codes, of which output is followed by a rate-1 recursive systematic convolutional code. The source correlation of all rows and columns of the 2D source is well exploited by using a modified Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm for the decoding of the BCH codes. Simulation results show that the proposed technique outperforms in terms of bit error rate the codes that exploits one-dimensional (1D) source correlation using the modified BCJR algorithm, and obviously the conventional system without source correlation exploitation. In order to further improve the performance, this article aims to make fine-tuning of the code parameters, given the source correlation property, that can achieve performance even closer to the theoretical limit than without the fine-tuning. Finally, results of image transmission simulations using two images, one having strong and the other weak 2D correlation, are presented to demonstrate the effectiveness of our proposed techniqu