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

Abstract

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.