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

Exploiting 2-dimensional source correlation in channel decoding with parameter estimation for unknown source

Source redundancy does not contain any significant information for transmission in a communication system and therefore, one of the approaches to overcome this issue is by exploiting the source redundancy for error-correction via Joint Source Channel Coding (JSCC) design. Existing JSCC systems were developed to exploit 1-Dimensional (1D) correlation exhibited by a source and later, a JSCC system exploiting 2-Dimensional (2D) source correlation known as the 2D JSCC system was introduced and had been proven to outperform the 1D JSCC system in terms of Bit Error rate (BER). However, the source correlation knowledge in the 2D JSCC system has been assumed to be perfectly known at the receiver. In a real communication system, source correlation knowledge may not always be available and thus, this research aims to develop a high performance 2D JSCC system for the unknown source correlation knowledge. A parameter estimation technique had been developed based on the Baum-Welsh algorithm and employed jointly with iterative channel decoding. Simulation results revealed that the proposed 2D JSCC system with parameter estimation (2D-JSCC-PET1) for an unknown source correlation knowledge can achieve performance very close to the ideal 2D JSCC system with a known source correlation knowledge by a difference of 0.05 dB. Furthermore, the proposed 2D-JSCC-PET1 system outperformed the benchmark 2D JSCC system using a different estimation technique (2D-JSCC-PET2) by 0.84 dB at a source correlation of p = 0.7 and the performance difference became larger with the increase of source correlation strength. The effectiveness of the proposed 2D-JSCC-PET1 system is demonstrated through image transmission simulations and the simulation results reveal that despite the correlation knowledge is unknown, the proposed 2D-JSCC-PET1 system can perform very close to the ideal 2D JSCC system with only 0.26 % and 0.06 % difference in Pixel-error percentage for an image exhibiting strong and weak correlation, respectively