Error-trellises for convolutional codes .II. Decoding methods

This letter considers the average complexity of maximum-likelihood (ML) decoding of convolutional codes. ML decoding can be modeled as finding the most probable path taken through a Markov graph. Integrated with the Viterbi algorithm (VA), complexity reduction methods often use the sum log likelihood (SLL) of a Markov path as a bound to disprove the optimality of other Markov path sets and to consequently avoid exhaustive path search. In this letter, it is shown that SLL-based optimality tests are inefficient if one fixes the coding memory and takes the codeword length to infinity. Alternatively, optimality of a source symbol at a given time index can be testified using bounds derived from log likelihoods of the neighboring symbols. It is demonstrated that such neighboring log likelihood (NLL)-based optimality tests, whose efficiency does not depend on the codeword length, can bring significant complexity reduction. The results are generalized to ML sequence detection in a class of discrete-time hidden Markov systems.

In this letter, we present a new decoder for quasi-orthogonal space-time block code (QOSTBC) based on conditional maximum-likelihood (ML) decoder. The proposed decoder results in the decoding complexity of O(L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> ), where L is the constellation size. In addition, by decomposing the real and imaginary components of the received signals, we further reduce the complexity of the decoder. Unlike many of previous decoders proposed for QOSTBC, our decoder is compatible with any complex-valued constellation such as quadrature amplitude modulation. Furthermore, the advantage of the proposed decoder with respect to other decoders is analyzed in terms of preprocessing and detection complexity. The simulation results show the identical bit error rate performance of the proposed and conventional ML decoder.

Maximum likelihood decoding of convolutional codes by Viterbi decoding method is an effective error correcting technique. However, little study on Viterbi decoding of high rate codes has been done since the number of paths entering each trellis state increases as the code rate increases. This paper presents a new maximum likelihood decoding of convolutional codes of rate (n ‐ 1)/n using a trellis. the trellis is drawn according to the state transitions of the syndrome former of the code, and has only one or two paths entering each state rather than 2n‐1 paths as in the case of conventional trellises. Thus, we can replace the 2n‐1‐ary comparisons at each state needed in the conventional Viterbi decoding for the code of rate (n ‐ 1)/n by binary comparisons. the implementation of maximum likelihood decoders for high rate convolutional codes is simplified greatly by this approach. Moreover, this decoding method shows the same performance as that of conventional Viterbi decoding method from the viewpoint of selecting the maximum likelihood code sequence.

This paper relates to the application method of channel state information (CSI) to the Viterbi (maximum likelihood) decoder in the digital terrestrial television broadcasting system. The proposed Viterbi decoder uses the CSI derived from the pilots inserted in the transmitter of the COFDM (coded orthogonal frequency division multiplexing) system. The CSI is calculated by interpolation using the pilots in the receiver. The active real (I) and imaginary (Q) data after equalization are transferred to the branch metric calculation block that decides the euclidean distance for soft decision decoding and also the estimated CSI values are transferred to the same block. After calculating the euclidean distance for the soft decision decoding, the euclidean distance of the branch metric is multiplied by CSI. To do so, new branch metric values that consider each carrier state information are obtained. We simulated this method in Rayleigh fading defined in the ETSI standard. From the simulation, this method has better performance of about 0.15 dB to 0.7 dB and 2.2 dB to 2.9 dB in the Rayleigh channel than that of conventional soft decision Viterbi decoding with or without a bit interleaver where the constellation is QPSK, l6-QAM and 64-QAM.

We introduce the notion of innovations for Viterbi decoding of convolutional codes. First, we define a kind of innovation corresponding to the received data, i.e., the input to a Viterbi decoder. Then, the structure of a scarce-state-transition (SST) Viterbi decoder is derived in a natural manner. It is shown that the newly defined innovation is just the input to the main decoder in an SST Viterbi decoder and generates the same syndrome as the original received data does. A similar result holds for quick-look-in codes as well. In this case, however, the precise innovation is not defined. We see that this innovation-like quantity is related to the linear smoothed estimate of the information. The essence of innovations approach to a linear filtering problem is first to whiten the observed data, and then to treat the resulting simpler white-noise observations problem. In our case, this corresponds to the reduction of decoding complexity in the main decoder in an SST Viterbi decoder. We show that the distributions related to the main decoder (i.e., the input distribution and the state distribution in the code trellis for the main decoder) are much biased under moderately noisy conditions. We see that these biased distributions actually lead to the complexity reduction in the main decoder. Furthermore, it is shown that the proposed innovations approach can be extended to maximum-likelihood decoding of block codes as well.

In this paper, soft-decision decoding is considered as an optimisation problem and a computationally efficient soft-decision decoding algorithm for block codes is developed using a compact Genetic Algorithm. The performance of this algorithm is investigated and compared with other decoding schemes. The results show the proposed algorithm gives large gains over the Generalised Minimum Distance (GMD) decoding algorithm and algebraic hard-decision decoding in flat Rayleigh fading. Further, the proposed algorithm achieves near maximum likelihood decoder (MLD) performance for the codes simulated. The complexity of this algorithm is also discussed and compared to the standard Order-l reprocessing algorithm.

Convolutional coding and Viterbi decoding, along with binary phase-shift keyed modulation, is presented as an efficient system for reliable communication on power limited satellite and space channels. Performance results, obtained theoretically and through computer simulation, are given for optimum short constraint length codes for a range of code constraint lengths and code rates. System efficiency is compared for hard receiver quantization and 4 and 8 level soft quantization. The effects on performance of varying of certain parameters relevant to decoder complexity and cost is examined. Quantitative performance degradation due to imperfect carrier phase coherence is evaluated and compared to that of an uncoded system. As an example of decoder performance versus complexity, a recently implemented 2-Mbit/s constraint length 7 Viterbi decoder is discussed. Finally a comparison is made between Viterbi and sequential decoding in terms of suitability to various system requirements.

In this study, a power-efficient very large-scale integration (VLSI) implementation for the convolutional code decoder is presented. Based on the state transparent convolutional code definition, the receiving codewords are classified into non-erroneous and erroneous segments separately. Different from the conventional Viterbi decoder (VD), the authors use a low-complexity decoder, denoted as bit reverse decoder, to recover the non-erroneous segments using reverse operation with a little power consumption and present the segment-based VD to decode the erroneous codeword segments. Then, the clock-gating technique is employed to switch between segment-based VD and bit reverse decoder for power saving. To further reduce the power consumption, the authors group registers into several segments in the survivor memory unit of the segment-based VD and also apply clock gating to each segment individually. According to the number of consecutive erroneous codeword segments, the corresponding numbers of register segments in the survivor memory unit are enabled and other register segments are clock-gated to reduce the switching activities. Besides, our design determines the start and terminal states of the survivor path to obtain correct results of erroneous segments without bit-error rate degradation. As compared with other decoders, our design requires less power without decreasing the decoding performance.

In this work, an efficient approach is introduced for reliability-based list decoding of a linear block code. This method terminates the decoding if a local optimal candidate satisfies a probabilistic sufficient condition. The average computation complexity is greatly reduced with this method. The false alarm probability associated with the use of the probabilistic sufficient condition is also derived. Simulation results confirm the analysis with no performance degradation and important computation savings on average for soft decision decoding of the (255,239) RS code (reduction by a factor between 2 and 20).

In order to realize a higher-code-gain forward error correction scheme in mobile satellite communication systems, a novel concatenated coding scheme employing soft decision decoding for not only inner codes but also outer codes (double soft decision, or DSD, concatenated forward error correction scheme) is proposed. Soft-decision outer decoding can improve the bit error probability of inner decoded data. In this scheme, likelihood information from an inner Viterbi decoder is used in the decoding of outer codes. A technique using the path memory circuit status 1.0 ratio for likelihood information is proposed, and is shown to be the most reliable even though it requires the simplest hardware among the alternative methods. A computer simulation clarifies that the DSD scheme improves Pe performance to one-third of that of the conventional hard-decision outer decoding. Moreover, to reduce the interleaving delay time in fading channels or inner decoded data of concatenated codes, a parallel forward error correction scheme is proposed. >

In this paper, we propose a near maximum likelihood (ML) decoding technique, which reduces the computational complexity of the exact ML decoding algorithm. The computations needed for the tree search in the ML decoding is simplified by reducing the dimension of the search space prior to the tree search. In order to compensate performance loss due to the dimension reduction, a list stack algorithm (LSA) is considered, which produces a list of the top K closest points. The combination of both approaches, called reduced dimension list stack algorithm (RD-LSA), is shown to provide flexibility and offers a performance-complexity trade-off. Simulations performed for V-BLAST transmission demonstrate that significant complexity reduction can be achieved compared to the sphere decoding algorithm (SDA) while keeping the performance loss below an acceptable level.

We introduce a very efficient (linear-time) method for finding the best metrics from among a number of contenders during breadth-first decoding of block or convolutional codes. This method can provide a sorted set of survivor metrics during M algorithm decoding of binary linear block codes using only /spl Lambda/1 comparisons in the worst case. This is a significant improvement over comparison-based sorting, which requires O(M log/sub 2/ M) comparisons. A similar improvement is attained over Hadian and Sohel's comparison-based selection method (where the best metrics are found without regard to order). The method is also readily applicable to the decoding of convolutional codes. >

This paper studies how to efficiently update the saddle-point strategy, or security strategy of one player in a matrix game when the other player develops new actions in the game. It is well known that the saddle-point strategy of one player can be computed by solving a linear program. Developing a new action will add a new constraint to the existing LP. Therefore, our problem becomes how to efficiently solve the new LP with a new constraint. Considering the potentially huge number of constraints, which corresponds to the large size of the other player’s action set, we use the shadow vertex simplex method, whose computational complexity is lower than linear with respect to the size of the constraints, as the basis of our iterative algorithm. We first rebuild the main theorems in the shadow vertex method with a relaxed non-degeneracy assumption to make sure such a method works well in our model, then analyze the probability that the old optimum remains optimal in the new LP, and finally provide the iterative shadow vertex method whose average computational complexity is shown to be strictly less than that of the shadow vertex method. The simulation results demonstrate our main results about the probability of re-computing the optimum and the computational complexity of the iterative shadow vertex method.

Tighter upper bounds on the error event and the bit error probabilities, respectively, for maximum-likelihood decoding of binary convolutional codes on the binary symmetric channel are derived from upper bounds previously published by Viterbi [1]. The measured bit error rate <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P_b</tex> for a constraint length 3 decoder has been plotted versus the channel transition probability <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</tex> and shows close agreement with the improved bound on the bit error probability.

Under successive cancellation (SC) decoding, polar codes are inferior to other codes of similar blocklength in terms of frame error rate. While more sophisticated decoding algorithms such as list- or stack-decoding partially mitigate this performance loss, they suffer from an increase in complexity. In this paper, we describe a new flavor of the SC decoder, called the SC flip decoder. Our algorithm preserves the low memory requirements of the basic SC decoder and adjusts the required decoding effort to the signal quality. In the waterfall region, its average computational complexity is almost as low as that of the SC decoder.