Iterative Joint Channel Decoding Research Articles

Iterative joint channel decoding of correlated sources

In this article we exploit the potential correlation existing between multiple information sources to achieve additional coding gains from the channel codes used for data protection. We do not assume the existence of, nor do we use channel side-information at the receiver. Instead, empirical estimates of the cross-correlation are used in partial decoding steps in an iterative joint soft decoding paradigm. Experimental results suggest that relatively few iterations (2 to 4) are sufficient to reap significant gains using this approach specially when the sources are highly correlated. Finally, we provide analytical performance bounds of the proposed technique showing a close match with the simulation results at sufficiently high SNR

LDPC-based channel coding of correlated sources with iterative joint decoding

This letter considers low-density parity-check (LDPC) coding of correlated binary sources and a novel iterative joint channel decoding without communication of any side information. We demonstrate that depending on the extent of the source correlation, additional coding gains can be obtained. Two stages of iterative decoding are employed. During global iterations, updated estimates of the source correlation are obtained and passed on to the sum-product decoder that performs local iterations with a predefined stopping criterion and/or a maximum number of local decoding iterations. Simulation results indicate that very few global iterations (2-5) are sufficient to reap significant benefits from implicit knowledge of source correlation. Finally, we provide analytical performance bounds for our iterative joint decoder and comparisons with sample simulation results.

Iterative Joint Channel Decoding of Correlated Sources Employing Serially Concatenated Convolutional Codes

This correspondence looks at the problem of joint decoding of serially concatenated convolutional codes (SCCCs) used for channel coding of multiple correlated sources. We assume a simple model whereby two correlated sources transmit SCCC encoded data to a single destination receiver. We do not assume the existence of, nor do we use channel side information at the receiver. In particular, we present a novel iterative joint channel decoding algorithm for correlated sources by using the empirical cross-correlation measurements at successive decoding iterations to provide extrinsic information to the outer codes of the SCCC configuration. Two levels of soft metric iterative decoding are used at the receiver: 1) iterative maximum a posteriori probability (MAP) decoding is used for efficient decoding of individual SCCC codes (local iterations) and 2) iterative extrinsic information feedback generated from the estimates of the empirical cross correlation in partial decoding steps is used to pass soft information to the outer decoders of the global joint SCCC decoder (global iterations). We provide analytical results followed by simulation studies confirming the robustness of the cross-correlation estimates to channel-induced errors, justifying the use of such estimates in iterative decoding. Experimental results suggest that relatively few global iterations (two to five) during which multiple local iterations are conducted are sufficient to reap significant gains using this approach specially when the sources are highly correlated.