In this paper, we propose a computationally efficient decoding algorithm for space-time trellis codes in slow Rayleigh fading channels. The proposed scheme is based on a stack algorithm with two key ideas: (i) a varia...
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In this paper, we propose a computationally efficient decoding algorithm for space-time trellis codes in slow Rayleigh fading channels. The proposed scheme is based on a stack algorithm with two key ideas: (i) a variable stack size depending upon the signal-to-noise ratio to avoid the exhaustive search of paths and (ii) a normalized metric, which is defined as each cumulative path metric divided by its own length in the stack, to provide an appropriate comparison of the paths with different lengths. Simulation results demonstrate that the proposed algorithm achieves near-ML performance with significant reduction in complexity, compared with the conventional Viterbi algorithm.
The performance of a receiver using a combined MLSE equalizer/decoder and D-diversity reception is analyzed for multipath Rayleigh fading channels. A new upper bound on the (decoded) bit error probability is derived. ...
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The performance of a receiver using a combined MLSE equalizer/decoder and D-diversity reception is analyzed for multipath Rayleigh fading channels. A new upper bound on the (decoded) bit error probability is derived. Comparisons to simulation results show that this upper bound is quite tight when the system has a high signal-to-noise ratio or when diversity reception is employed. The upper bound involves an infinite series that must be truncated at a point where the remainder can be safely assumed to be small. An algorithm which is based on a one-directional stack algorithm is proposed for this calculation, because it makes efficient use of computer memory.
We consider a compressed sensing problem to recover a sparse signal vector from a small number of one-bit quantized and noisy measurements. In this system, a probabilistic greedy algorithm, called bayesian matching pu...
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We consider a compressed sensing problem to recover a sparse signal vector from a small number of one-bit quantized and noisy measurements. In this system, a probabilistic greedy algorithm, called bayesian matching pursuit (BMP), has been recently proposed in which a new support index is identified for each iteration, via a local optimal strategy based on a Gaussian-approximated maximum a posteriori estimation. Although BMP can outperform the other existing methods as Quantized Compressive Sampling Matched Pursuit (QCoSaMP) and Quantized Iterative Shrinkage-Thresholding algorithm (QISTA), its accuracy is still far from the optimal, yielding a locally optimal solution. Motivated by this, we propose an advanced greedy algorithm by leveraging the idea of a stack algorithm, which is referred to as stacked BMP (StBMP). The key idea of the proposed algorithm is to store a number of candidate partial paths (i.e., the candidate support sets) in an ordered stack and tries to find the global optimal solution by searching along the best path in the stack. The proposed method can efficiently remove unnecessary paths having lower path metrics, which can provide a lower complexity. Simulation results demonstrate that the proposed StBMP can significantly improve the BMP by keeping a low computational complexity.
Sequential decoding of short length binary codes for the additive white Gaussian noise channel is considered. A variant of the variable-bias term (VBT) metric is introduced, producing useful trade-offs between perform...
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Sequential decoding of short length binary codes for the additive white Gaussian noise channel is considered. A variant of the variable-bias term (VBT) metric is introduced, producing useful trade-offs between performance and computational complexity. Comparisons are made with tail-biting convolutional codes decoded with a wrap-around Viterbi algorithm (WAVA) and with polar codes under successive-cancellation list (SCL) decoding. It is found that sequential decoding with the improved VBT metric has a better performance-complexity tradeoff than tail-biting codes under WAVA decoding (except at low complexities) but a worse performance-complexity tradeoff than polar codes under SCL decoding (except at high complexities).
In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood ...
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In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single best searching algorithm called sphere decoding, and 2) the recomputation of a set of weak symbols, i. e., those ignored in the reduced dimension search, for each strong symbol candidate found during the list tree search. Through simulations on M-quadrature amplitude modulation (QAM) transmission in frequency nonselective multi-input-multioutput (MIMO) channels, we demonstrate that the RD-MLS algorithm shows near constant complexity over a wide range of bit error rate (BER) (10(-1) similar to 10(-4)), while limiting performance loss to within 1 dB from ML detection.
An algorithm is proposed for universal decoding of convolutional/trellis codes employed over unknown channels. On discrete memoryless channels and at rates below the channel's computational cutoff rate (for a unif...
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An algorithm is proposed for universal decoding of convolutional/trellis codes employed over unknown channels. On discrete memoryless channels and at rates below the channel's computational cutoff rate (for a uniform input distribution), the algorithm achieves an asymptotic complexity-performance tradeoff similar to the tradeoff achieved by the Viterbi algorithm, but with the benefit that the algorithm's implementation does not require knowledge of the channel law. The algorithm is also applicable to channels with memory, and in particular to intersymbol interference (ISI) channels, to channels with nonlinear ISI, and even to general finite-state channels.
The decoding of long memory high-rate punctured convolutional codes by sequential decoding algorithms is investigated. Both the stack and the Fano algorithms have been thoroughly tested through computer simulation wit...
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The decoding of long memory high-rate punctured convolutional codes by sequential decoding algorithms is investigated. Both the stack and the Fano algorithms have been thoroughly tested through computer simulation with coding rates ranging from R = 2/3 to R = 7/8. Error and overflow probabilities and variability of decoding effort are similar for both algorithms. With hard quantization, plateaus appear in the cumulatives of decoding effort for both algorithms. Comparing the punctured approach of decoding to the more traditional technique for high-rate codes, it is found that punctured decoders perform a larger number of simpler computations, so that the overall decoding effort is on the average more important for the usual decoder than it is for its punctured counterpart. Finally, computational variability, error and overflow probabilities are no worse for punctured decoders than they are for normal decoders.
Cursive script word recognition is the problem of transforming a word from the iconic form of cursive writing to its symbolic form. Several component processes of a recognition system for isolated offline cursive scri...
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Cursive script word recognition is the problem of transforming a word from the iconic form of cursive writing to its symbolic form. Several component processes of a recognition system for isolated offline cursive script words are described. A word image is transformed through a hierarchy of representation levels: points, contours, features, letters, and words. A unique feature representation is generated bottom-up from the image using statistical dependences between letters and features. Ratings for partially formed words are computed using a stack algorithm and a lexicon represented as a trie. Several novel techniques for low- and intermediate-level processing for cursive script are described, including heuristics for reference line finding, letter segmentation based on detecting local minima along the lower contour and areas with low vertical profiles, simultaneous encoding of contours and their topological relationships, extracting features, and finding shape-oriented events. Experiments demonstrating the performance of the system are also described.
The performance of ideal reduced-state sequence estimation (RSSE) (without error propagation) is known as a good approximation to the performance of real RSSE. In the literature, the minimum distance of ideal RSSE has...
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The performance of ideal reduced-state sequence estimation (RSSE) (without error propagation) is known as a good approximation to the performance of real RSSE. In the literature, the minimum distance of ideal RSSE has been employed for approximating the error probability of real RSSE. However, this approximation can be very poor, even though the system has a large signal-to-noise ratio. In this paper, a union upper bound on the error probability for ideal RSSE is used to approximate the true error probability. This union bound provides a better approximation than the minimum distance. A new method based on a stack algorithm and a subset-error state diagram is proposed for calculating this union bound. The stack algorithm is employed because it provides a good tradeoff between computer memory and computing time.
The error probability of reduced-state sequence estimation (RSSE) for trellis-coded modulation (TCM) on intersymbol interference channels is evaluated. A method based on a stack algorithm is proposed to evaluate the u...
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The error probability of reduced-state sequence estimation (RSSE) for trellis-coded modulation (TCM) on intersymbol interference channels is evaluated. A method based on a stack algorithm is proposed to evaluate the union bound on the error probability for ideal RSSE which is a good approximation to the error probability of real RSSE. The stack algorithm is employed because it provides a good tradeoff between computer memory and computing time.
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