Reliability Information in Channel Decoding: Practical Aspects and Information Theoretical Bounds
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This thesis addresses the use of reliability information in channel decoding and covers practical aspects as well as information-theoretical bounds.The considered transmission systems comprise linear binary channel encoders, symmetric memoryless communication channels, and non-iterative or iterative symbol-by-symbol soft-output channel decoders.The notions of accurate and mismatched reliability values are introduced, and the measurement and improvement of the quality of reliability values are discussed.A criterion based on the Kullback-Leibler distance is proposed to assess the difference between accurate and mismatched reliability values.The concepts are applied to iterative decoders for parallel concatenated codes.Accurate reliability values may be exploited to estimate transmission quality parameters, such as the bit error probability or the symbol-wise mutual information between encoder input and decoder output.The proposed method is unbiased and does not require knowledge of the t
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This thesis addresses the use of reliability information in channel decoding and covers practical aspects as well as information-theoretical bounds.The considered transmission systems comprise linear binary channel encoders, symmetric memoryless communication channels, and non-iterative or iterative symbol-by-symbol soft-output channel decoders.The notions of accurate and mismatched reliability values are introduced, and the measurement and improvement of the quality of reliability values are discussed.A criterion based on the Kullback-Leibler distance is proposed to assess the difference between accurate and mismatched reliability values.The concepts are applied to iterative decoders for parallel concatenated codes.Accurate reliability values may be exploited to estimate transmission quality parameters, such as the bit error probability or the symbol-wise mutual information between encoder input and decoder output.The proposed method is unbiased and does not require knowledge of the t
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