Jinhong Wu

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—Near-capacity performance of turbo codes is generally achieved with a large number of decoding iterations. Various iteration stopping rules introduced in the literature often induce performance loss. This paper proposes a novel partial decoding iteration scheme using a bit-level convergence test. We first establish decoding optimality of windowed partial(More)
—Based on an analysis on the recursive computation of the iterative maximum a posteriori (MAP) algorithm for turbo decoding, this paper considers a modified MAP scheme with reduced block lengths for symbols with unreliable detection after some initial iterations. Applying symbol selection based on cross-entropy measurement for parallel concatenated(More)
—Cross-entropy based symbol selection and partial iterative decoding for serial concatenated convolutional codes (SCCC) is developed in this paper. We first apply symbol classification in terms of detection convergence status to all coded bits of the outer code. After that, computations are focused on symbols that have not reached convergence, by applying(More)
—We consider bit interleaved coded modulation (BICM) receiver performance improvement based on the concept of generalized mutual information (GMI). Increasing achievable rates of BICM receiver with GMI maximization by proper scaling of the log likelihood ratio (LLR) is investigated. While it has been shown in the literature that look-up table based LLR(More)
Iterative detection and decoding (IDD) relies on passing useful extrinsic information between the detector and the decoder. Due to the sub-optimality of practical detector and/or decoder, the direct output LLRs from the detector or the decoder may not provide sufficient gains to each other. Proper scaling of the extrinsic LLRs based on certain optimality(More)