The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Source-controlled channel decoding
Abstract
Source and channel coding have been treated separately in most cases. It can be observed that most source coding algorithms for voice, audio and images still have correlation in certain bits. Transmission errors in these bits usually account for the significant errors in the reconstructed source signal. This paper proposes a modification of the Viterbi decoding algorithm (VA) for binary trellises which uses a priori or a posteriori information about the source bit probability for better decoding in addition to soft inputs and channel state information. Analytical upper bounds for the BER of convolutional codes for this modified VA (APRI-VA) are given. The algorithm is combined with the soft output viterbi algorithm (SOVA) and an estimator for the residual correlation of the source bits to achieve source-controlled channel decoding for framed source bits. The description is simplified by an algebra for the log-likelihood ratio L(u)=log(P(u=+1)/P(u=-1)) which allows a clear definition of the "soft" values of source-, channel-, and decoded bits as well as a simplified description of the traceback version of the SOVA. Applications are given for PCM transmission and the full rate GSM speech codec. For an PCM coded oversampled bandlimited Gaussian source transmitted over Gaussian and Rayleigh channels with convolutional codes the decoding errors are reduced by a factor of 4 to 5 when the APRI-SOVA is used instead of the VA. A simple dynamic Markov correlation estimator is used. With these receiver-only modifications the channel SNR in a bad mobile environment can be lowered by 2 to 4 dB resulting in the same voice quality. Further applications are briefly discussed.>
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Citations
Cites
Forney, G. D. (1973) · Proceedings of the IEEE · doi:10.1109/proc.1973.9030 · Cited by 5,647
Optimal decoding of linear codes for minimizing symbol error rate (Corresp.)
Bahl, L., Cocke, J., Jelinek, F., & Raviv, J. (1974) · IEEE Transactions on Information Theory · doi:10.1109/tit.1974.1055186 · Cited by 5,208
Optimal decoding of linear codes for minimizing symbol error rate
Bahl, L. R. (1974) · IEEE Transactions on Information Theory · Cited by 4,665
Rate-compatible punctured convolutional codes (RCPC codes) and their applications
Hagenauer, J. (1988) · IEEE Transactions on Communications · doi:10.1109/26.2763 · Cited by 1,827
Self-citationCombined Source-Channel Coding of Images Using the Block Cosine Transform
Modestino, J. W., Daut, D. G., & Vickers, A. L. (1981) · IRE Transactions on Communications Systems · doi:10.1109/tcom.1981.1095155 · Cited by 128
Subband speech coding and matched convolutional channel coding for mobile radio channels
Cox, R. V., Hagenauer, J., Seshadri, N., & Sundberg, C.-E. W. (1991) · IEEE Transactions on Signal Processing · doi:10.1109/78.91143 · Cited by 97
Self-citation
Cited by
Optical Communication in Space: Challenges and Mitigation Techniques
Kaushal, H., & Kaddoum, G. (2017) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2016.2603518 · Cited by 1,927
Metadata
- Authors
- Hagenauer, J.
- Venue
- IEEE Transactions on Communications
- Published
- 1995
- Volume
- 43
- Issue
- 9
- Pages
- 2449-2457
- ISSN
- 0090-6778 (ISSN-L)
- OpenAlex
- W2167393257
- Cited by
- 448
- References
- 6
- FWCI
- 15.68
- Type
- journal article
- Language
- en
- Open access
- closed
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