List-sequence (LS) decoding has the potential to yield significant coding gain additional to that of conventional single-sequence decoding, and it can be implemented with full backward compatibility in systems where an error-detecting code is concatenated with an error-correcting code. LS maximum-likelihood (ML) decoding provides a list of estimated sequences in likelihood order. For convolutional codes, this list can be obtained with the serial list Viterbi algorithm (SLVA). Through modification of the metric increments of the SLVA, an LS maximum a posteriori (MAP) probability decoding algorithm is obtained that takes into account bitwise a priori probabilities and produces an ordered list of sequence MAP estimates. The performance of the resulting LS-MAP decoding algorithm is studied in this paper. Computer simulations and approximate analytical expressions, based on geometrical considerations of the decision domains of LS decoders, are presented. We focus on the frame-error performance of LS-MAP decoding, with genie-assisted error detection, on the additive white Gaussian noise channel. It is concluded that LS-MAP decoding exploits a priori information more efficiently, in order to achieve performance improvements, than does conventional single-sequence MAP decoding. Interestingly, LS-MAP decoding can provide significant improvements at low signal-to-noise ratios, compared with LS-ML decoding. In this environment, it is furthermore observed that feedback convolutional codes offer performance improvements over their feedforward counterparts. Since LS-MAP decoding can be implemented in existing systems at a modest complexity increase, it should have a wide area of applications, such as joint source-channel decoding and other kinds of iterative decoding.

We present a soft decoding algorithm for convolutional codes that simultaneously yields soft-sequence output, i.e., list sequence (LS) decoding, and soft-symbol output. The max-log list algorithm (MLLA) introduced in this paper provides near-optimum soft-symbol output equal to that of the max-log maximum a posteriori (MAP) probability algorithm. Simultaneously, the algorithm produces an ordered list containing LS-MAP estimates. The MLLA exists in an optimum and a suboptimum version that are different in that the optimum version produces optimum LS-MAP decoding for arbitrary list lengths, while the suboptimum low-complexity version only provides the MAP, the second-order MAP, and the third-order MAP sequence estimates. For lists with more than three elements, MAP decoding is not guaranteed, but the LS decoding is close to the optimal. It is demonstrated that the suboptimum/optimum MLLA can be used to obtain the combination of soft-symbol and soft-sequence outputs at lower complexity than a previously published algorithm. Furthermore, the suboptimum MLLA is well suited for operation in an iterative list (turbo) decoder, since it is obtained by only minor modifications of the well-known Max-Log-MAP algorithm frequently used for decoding of the component codes of turbo codes. Another potential area of application for the suboptimum/optimum MLLA is joint source-channel LS decoding. Estimates of complexity and memory use, as well as performance evaluations of the suboptimum/optimum MLLA, are provided in this paper.

An algorithm for decoding Turbo codes that combines conventional Turbo decoding and list sequence maximum a posteriori probability decoding is presented and evaluated. Compared to previous results on this theme, performance improvements in the order of 0.7 dB are obtained for Turbo codes with 514-b pseudorandom interleaving at a frame error rate of 10/sup -4/ on the additive white Gaussian noise channel.

An algorithm for decoding turbo codes that combines conventional turbo decoding and list sequence (LS) maximum a posteriori probability (MAP) decoding is presented and evaluated. Compared to previous results on this theme, a reduction in the order of 0.7 dB of the signal-to-noise ratio (SNR) is obtained for turbo codes with 514-bit pseudo-random interleaving at frame error rate (FER) 10/sup -4/ on the additive white Gaussian noise (AWGN) channel.

The throughput performance of incremental redundancy (INR) schemes, based on short constraint length convolutional codes, is evaluated for the block-fading Gaussian collision channel. Results based on simulations and union bound computations are compared to estimates of the achievable throughput performance with random binary and Gaussian coding in the limit of large block lengths, obtained through information outage considerations. For low channel loads, it is observed that INR schemes with binary convolutional codes and limited block length may provide throughput close to the achievable performance for binary random coding. However, for these low loads, compared to binary random coding, Gaussian random coding may provide significantly better throughput performance, which prompts the use of larger modulation constellations. For high channel loads, a relatively large gap in throughput performance between binary convolutional codes and binary random codes indicates a potential for extensive performance improvement by alternative coding strategies. Only small improvements of the throughput have been observed by increasing the complexity through increased state convolutional coding.

A list sequence (LS) maximum a posteriori probability (MAP) decoding algorithm for convolutional codes, that takes into account bitwise a priori probabilities and produces a rank ordered list of /spl Lscr/ sequence MAP estimates, can be obtained by modification of the metric increments of the serial list Viterbi algorithm. In this paper, we study the performance of LS-MAP decoding with genie-assisted error detection on the additive white Gaussian noise channel. Computer simulations and approximate analytical expressions, based on geometrical considerations are presented. We focus on the frame error rate and it is concluded that LS-MAP decoding with /spl Lscr/ > 1 often exploits a priori information more efficiently than conventional single sequence MAP decoding (/spl Lscr/ = 1). This leads in many cases to larger relative gains with LS-MAP than LS-ML decoding as /spl Lscr/ increases.

We evaluate the performance of list sequence (LS) maximum a posteriori probability (MAP) decoding on the additive white Gaussian noise (AWGN) channel and the fully interleaved flat Rayleigh fading channel. In particular, we focus on the frame error rate (FER) of LS-MAP decoding with genie-assisted error detection. It is demonstrated that LS-MAP decoding yields significant performance improvements relative to single sequence MAP decoding on the AWGN channel as well as the fully interleaved flat Rayleigh fading channel. Furthermore, feedback convolutional codes have been observed to have the potential to yield improved FER performance with both single sequence MAP and IS-MAP decoding, compared to feedforward convolutional codes. Based on these performance improvements, we propose a joint source-channel IS decoding scheme. The scheme is based on the max-log list algorithm for simultaneous soft symbol and IS decoding of convolutional codes. In existing systems where a cyclic redundancy check for error mitigation is concatenated with a convolutional code for error correction, this scheme can be implemented in a fully backward compatible receiver.