Low Complexity Decoding for Punctured Trellis-Coded Modulation Over Intersymbol Interference Channels

Classical trellis-coded modulation (TCM) as introduced by Ungerboeck in 1976/1983 uses a signal constellation of twice the cardinality compared to an uncoded transmission with one bit of redundancy per PAM symbol, i.e., application of codes with rates $\frac{n-1}{n}$ when $2^{n}$ denotes the cardinality of the signal constellation. The original approach therefore only comprises integer transmission rates, i.e., $R=\left\{ 2,\,3,\,4\,\ldots \right\}$, additionally, when transmitting over an intersymbol interference (ISI) channel an optimum decoding scheme would perform equalization and decoding of the channel code jointly. In this paper, we allow rate adjustment for TCM by means of puncturing the convolutional code (CC) on which a TCM scheme is based on. In this case a nontrivial mapping of the output symbols of the CC to signal points results in a time-variant trellis. We propose an efficient technique to integrate an ISI-channel into this trellis and show that the computational complexity can be significantly reduced by means of a reduced state sequence estimation (RSSE) algorithm for time-variant trellises.Comment: 4 pages, 7 pictured, accepted for 2014 International Zurich Seminar on Communication

Reduced Complexity Super-Trellis Decoding for Convolutionally Encoded Transmission Over ISI-Channels

In this paper we propose a matched encoding (ME) scheme for convolutionally encoded transmission over intersymbol interference (usually called ISI) channels. A novel trellis description enables to perform equalization and decoding jointly, i.e., enables efficient super-trellis decoding. By means of this matched non-linear trellis description we can significantly reduce the number of states needed for the receiver-side Viterbi algorithm to perform maximum-likelihood sequence estimation. Further complexity reduction is achieved using the concept of reduced-state sequence estimation.Comment: 6 pages, 8 figures, accepted for ICNC'13. (see also: arXiv:1205.7031

A novel MLSD receiver architecture for nonlinear channels

A new architecture for maximum likelihood sequence detec- tion (MLSD) in nonlinear dispersive channels (NLCs) is presented, and its robustness to inaccurate channel knowledge is analyzed. This architecture is developed by considering a novel orthogonal representation of the NLC, which is exploited to develop a front-end capable of obtaining uncorrelated symbol rate samples, representing a sufficient statistic for information decoding. This front-end is a special form of space-time whitened matched filter (ST-WMF), and the MLSD obtained by using this front-end (ST-WMF-MLSD) requires simple branch metrics due to the signal whitening. The ST-WMF also allows for space-time compression of the equivalent channel, which is exploited for further complexity reduction of the ST-WMF-MLSD. Simulation results show the good trade-off in performance and complexity obtained with the ST-WMF- MLSD, even in the presence of inaccurate channel knowledge.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Optimal Sequence Estimation for Convolutionally Coded Signals With Binary Digital Modulation in ISI Channels

Decoding convolutional codes with binary digital modulation in intersymbol interference (ISI) channels is studied. The receiver structure is a whitened matched filter (WMF) whose transfer function is determined by the ISI channel. Decoding of the output sequence can be performed in two steps or one step. The two-step decoding first decodes the ISI corrupted coded sequence back to the ISI free coded sequence which is then decoded back to the uncoded message sequence. For one-step decoding, the entire encoder-channel-receiver system is modeled as a new encoder with combined memory length of the memory lengths of the original encoder and the channel, and followed by a weighted summation mapping from the binary symbols to real number symbols. The weighting coefficients are determined by the channel characteristic. In both two-step and one-step decoding, the Viterbi algorithm (VA) is used to perform the maximum likelihood decoding. Decoding error probability and complexity of both methods are analyzed, simulated and compared

Optimal Sequence Estimation for Convolutionally Coded Signals With Binary Digital Modulation in ISI Channels

Decoding convolutional codes with binary digital modulation in intersymbol interference (ISI) channels is studied. The receiver structure is a whitened matched filter (WMF) whose transfer function is determined by the ISI channel. Decoding of the output sequence can be performed in two steps or one step. The two-step decoding first decodes the ISI corrupted coded sequence back to the ISI free coded sequence which is then decoded back to the uncoded message sequence. For one-step decoding, the entire encoder-channel-receiver system is modeled as a new encoder with combined memory length of the memory lengths of the original encoder and the channel, and followed by a weighted summation mapping from the binary symbols to real number symbols. The weighting coefficients are determined by the channel characteristic. In both two-step and one-step decoding, the Viterbi algorithm (VA) is used to perform the maximum likelihood decoding. Decoding error probability and complexity of both methods are analyzed, simulated and compared

A novel MLSD receiver architecture for nonlinear channels

A new architecture for maximum likelihood sequence detec- tion (MLSD) in nonlinear dispersive channels (NLCs) is presented, and its robustness to inaccurate channel knowledge is analyzed. This architecture is developed by considering a novel orthogonal representation of the NLC, which is exploited to develop a front-end capable of obtaining uncorrelated symbol rate samples, representing a sufficient statistic for information decoding. This front-end is a special form of space-time whitened matched filter (ST-WMF), and the MLSD obtained by using this front-end (ST-WMF-MLSD) requires simple branch metrics due to the signal whitening. The ST-WMF also allows for space-time compression of the equivalent channel, which is exploited for further complexity reduction of the ST-WMF-MLSD. Simulation results show the good trade-off in performance and complexity obtained with the ST-WMF- MLSD, even in the presence of inaccurate channel knowledge.Sociedad Argentina de Informática e Investigación Operativa (SADIO