• ETRI Journal
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• v.28 no.3
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• pp.301-310
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• 2006

In mobile communications, a class of variable-complexity algorithms for convolutional decoding known as sequential decoding algorithms is of interest since they have a computational time that could vary with changing channel conditions. The Fano algorithm is one well-known version of a sequential decoding algorithm. Since the decoding time of a Fano decoder follows the Pareto distribution, which is a heavy-tailed distribution parameterized by the channel signal-to-noise ratio (SNR), buffers are required to absorb the variable decoding delays of Fano decoders. Furthermore, since the decoding time drawn by a certain Pareto distribution can become unbounded, a maximum limit is often employed by a practical decoder to limit the worst-case decoding time. In this paper, we investigate the relations between buffer occupancy, decoding time, and channel conditions in a system where the Fano decoder is not allowed to run with unbounded decoding time. A timeout limit is thus imposed so that the decoding will be terminated if the decoding time reaches the limit. We use discrete-time semi-Markov models to describe such a Fano decoding system with timeout limits. Our queuing analysis provides expressions characterizing the average buffer occupancy as a function of channel conditions and timeout limits. Both numerical and simulation results are provided to validate the analytical results.

• The Korean Journal of Physiology and Pharmacology
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• v.13 no.3
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• pp.221-227
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• 2009

For successful restoration of visual function by a visual neural prosthesis such as retinal implant, electrical stimulation should evoke neural responses so that the informat.ion on visual input is properly represented. A stimulation strategy, which means a method for generating stimulation waveforms based on visual input, should be developed for this purpose. We proposed to use the decoding of visual input from retinal ganglion cell (RGC) responses for the evaluation of stimulus encoding strategy. This is based on the assumption that reliable encoding of visual information in RGC responses is required to enable successful visual perception. The main purpose of this study was to determine the influence of inter-dependence among stimulated RGCs activities on decoding accuracy. Light intensity variations were decoded from multiunit RGC spike trains using an optimal linear filter. More accurate decoding was possible when different types of RGCs were used together as input. Decoding accuracy was enhanced with independently firing RGCs compared to synchronously firing RGCs. This implies that stimulation of independently-firing RGCs and RGCs of different types may be beneficial for visual function restoration by retinal prosthesis.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.213-220
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• 2015

In this paper, we present a reliability-based iterative proportionality-logic decoding algorithm for two classes of structured low-density parity-check (LDPC) codes. The main contributions of this paper include: 1) Syndrome messages instead of extrinsic messages are processed and exchanged between variable nodes and check nodes, which can reduce the decoding complexity; 2) a more flexible decision mechanism is developed in which the decision threshold can be self-adjusted during the iterative process. Such decision mechanism is particularly effective for decoding the majority-logic decodable codes; 3) only part of the variable nodes satisfying the pre-designed criterion are involved for the presented algorithm, which is in the proportionality-logic sense and can further reduce the computational complexity. Simulation results show that, when combined with factor correction techniques and appropriate proportionality parameter, the presented algorithm performs well and can achieve fast decoding convergence rate while maintaining relative low decoding complexity, especially for small quantized levels (3-4 bits). The presented algorithm provides a candidate for those application scenarios where the memory load and the energy consumption are extremely constrained.

• IE interfaces
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• v.4 no.1
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• pp.63-69
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• 1991

In this paper, the Go-Back-N ARQ protocol with decoding in communication network is considered. The time delay and throughput are respectively analyzed as a function of window size and decoding time out. Packets arrive continuously at the decoder, and are stored in a buffer if the decoder is busy upon its arrival. The decoder devotes no more than a time-out period of predetermined length to the decoding of any single packet. If packet decoding is completed within that period, the packet leaves the system. Otherwise, it is retransimitted and its decoding starts anew. The time delay and throughput are obtained using recursive formula and difference equation. An appropriate time out and window size that satisfies the grade of service can be determined.

• The Journal of the Korea institute of electronic communication sciences
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• v.6 no.1
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• pp.20-26
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• 2011

In this paper, we propose an efficient algorithm for reducing the complexity of LDPC code decoding by using node monitoring (NM) and Piecewise Linear Function Approximation (NP). This NM algorithm is based on a new node-threshold method, and the message passing algorithm. Piecewise linear function approximation is used to reduce the complexity for more. This algorithm was simulated in order to verify its efficiency. Simulation results show that the complexity of our NM algorithm is reduced to about 20%, compared with thoes of well-known method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7A
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• pp.1080-1089
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• 1999

The union upper bounds to the bit error probability of maximum likelihood(ML) soft-decoding of parallel concatenated convolutional codes(PCCC) and serially concatenated convolutional codes(SCCC) can be evaluated through the weight enumerating function(WEF). This union upper bounds become the lower bounds of the BER achievable when iterative decoding is used. In this paper, to compute the WEF, an efficient error event search algorithm which is a combination of stack algorithm and bidirectional search algorithm is proposed. By computor simulation, it is shown that the union boounds obtained by using the proposed algorithm become the lower bounds to BER of concatenated convolutional codes with iterative decoding.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.6
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• pp.124-132
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• 1985

The functional testing of microprocessor comes to be time - consuming task with the progress of technology of LSl/VLSl . In this paper, we present an efficient method to test instruction decoding function of MC 68000 that is the reason of complicated functional testing. This method is based on the analysis of operation word that is instruction dccoding information available to user with microprocessor's manual. Thc instruction is partitioned into representative instructions and party instructions. Then 332 minimum test instruction pairs are chosen from 69 basic instructions for detecting of instruction decoding function faults and test procedure for these is discussed.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.11
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• pp.1231-1238
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• 2015

In this paper, we proposed an efficient algorithm using Node monitoring (NM) and Piecewise Linear Function Approximation(: NP) for reducing the complexity of LDPC code decoding. Proposed NM algorithm is based on a new node-threshold method together with message passing algorithm. Piecewise linear function approximation is used to reduce the complexity of the algorithm. This new algorithm was simulated in order to verify its efficiency. Complexity of our new NM algorithm is improved to about 20% compared with well-known methods according to simulation results.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.8
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• pp.74-83
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• 1997

In this paper, we propose a new fractal image decoding algorithm with fast convergence speed by using the data dependence and the improved initial image estimation. Conventional method for fractal image decoding requires high-degrdd computational complexity in decoding process, because of iterated contractive transformations applied to whole range blocks. On proposed method, Range of reconstruction imagte is divided into referenced range and data dependence region. And computational complexity is reduced by application of iterated contractive transformations for the referenced range only. Data dependence region can be decoded by one transformations when the referenced range is converged. In addition, more exact initial image is estimated by using bound () function in case of all, and an initial image more nearer to a fixed point is estimated by using range block division estimation. Consequently, the convergence speed of reconstruction iamge is improved with 40% reduction of computational complexity.

• Journal of Information Processing Systems
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• v.10 no.1
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• pp.69-80
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• 2014

The free distance of (n, k, l) convolutional codes has some connection with the memory length, which depends on not only l but also on k. To efficiently obtain a large memory length, we have constructed a new class of (2k, k, l) convolutional codes by (2k, k) block codes and (2, 1, l) convolutional codes, and its encoder and generation function are also given in this paper. With the help of some matrix modules, we designed a single structure Viterbi decoder with a parallel capability, obtained a unified and efficient decoding model for (2k, k, l) convolutional codes, and then give a description of the decoding process in detail. By observing the survivor path memory in a matrix viewer, and testing the role of the max module, we implemented a simulation with (2k, k, l) convolutional codes. The results show that many of them are better than conventional (2, 1, l) convolutional codes.