• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.8
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• pp.704-711
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• 2013

Applying channel state information feedback scheme based on codebook for FDD downlink MIMO system to massive MIMO system directly causes following two problems. First, codebook generation becomes hard because resources needed for codebook generation increases exponentially in the number of antennas. In addition, long time is needed for channel state information feedback because users's codebook search time increases linearly in the number of antennas. This paper proposes a codebook generation scheme and a codebook search scheme which can reduce resource for codebook generation and codebook search time drastically though they have similar performance as conventional approaches.

• Journal of Digital Contents Society
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• v.13 no.3
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• pp.309-315
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• 2012

Active data are the input data which are changed its membership as Vector Quantization codebook generation algorithm is processed. In the process of VQ codebook generation algorithm performed, the actual active data out of the entire input data will be less presented as the process is performed. Therefore, if we can accurately find the active data and only if we are going to do VQ codebook generation on the active data, then we can significantly reduce the overall generation time. In this paper, we presented the triangle inequality based algorithm to select the active data. Experimental results show that our algorithm is superior to other methods in terms of the VQ codebook generation time.

• Journal of Digital Contents Society
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• v.16 no.3
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• pp.373-379
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• 2015

In this paper, we propose an online VQ Codebook generation method for updating an existing VQ Codebook in real-time and adding to an existing cluster with newly created text data which are news paper, web pages, blogs, tweets and IoT data like sensor, machine. Without degrading the performance of the batch VQ Codebook to the existing data, it was able to take advantage of the newly added data by using a triangle inequality which modifying the VQ Codebook progressively show a high degree of accuracy and speed. The result of applying to test data showed that the performance is similar to the batch method.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.3
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• pp.205-209
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• 2004

The K-means algorithm is the most widely used method among the codebook generation algorithms in vector quantization. In this paper, we propose a codebook generation algorithm using a new updating condition to enhance the codebook performance. The conventional K-means algorithm uses a fixed weight of the distance for all training iterations, but the proposed method uses different weights according to the updating condition from the new codevectors for training iterations. Then, different weights can be applied to generate codevectors at each iteration according to this condition, and it can have a similar effect to variable weights. Experimental results show that the proposed algorithm has the better codebook performance than that of K-means algorithm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.3
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• pp.155-157
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• 2014

In this paper, a novel codebook generation strategy is proposed. With the given codebooks, two codeword selection procedures are proposed and analyzed for generating the quantized multiple-input multiple-output (MIMO) channel state information (CSI). Furthermore, three different quantization and normalization strategies are analyzed. The simulation results suggest that the proposed 'quantized channel generation method 2' is the best strategy to reduce the quantization and normalization errors to generate the final quantized MIMO CSI.

• Journal of Digital Contents Society
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• v.15 no.3
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• pp.365-371
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• 2014

In the era of big data, algorithms for the existing IT environment cannot accept on a distributed architecture such as hadoop. Thus, new distributed algorithms which apply a distributed framework such as MapReduce are needed. Lloyd's algorithm commonly used for vector quantization is developed using MapReduce recently. In this paper, we proposed a decombined distributed VQ codebook generation algorithm based on a distributed VQ codebook generation algorithm using MapReduce to get a result more fast. The result of applying the proposed algorithm to big data showed higher performance than the conventional method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.4
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• pp.63-67
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• 2004

The conventional typical techniques as fast codebook generation methods are PDS, FNNS, and FC. In this paper, we propose FCNNPDS integrated the conventional methods to generate a codebook. The results of simulations show that the computational magnitude of FCNNPDS is reduced to 40-95% lower than conventional techniques. But comparison computation has no relation with k dimension of vectors, that is, because the computational magnitude of comparison is smaller than others, therefore FCNNPDS may be the best method than the conventional methods.

• Proceedings of the Korea Contents Association Conference
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• 2004.11a
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• pp.211-214
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• 2004

Former Vector Quantization-based digital watermarking was operated by the user codebook which is combined by the basic and extented codebook. The design way of extended codebook is very important, but still not proposed the concrete way. This paper proposes how to make the basic and extended codebook using the weight of distance. The proposed way can always get the standardizing codebook without inputing watermark.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.579-582
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• 2001

Vector Transformation is a new method unified vector quantization and coding. So far, codebook generation applied to coding was LBG algorithm. But using the advantage of SOFM(Self-Organizing Feature Map) based on neural network can improve a system's performance. In this paper, we generated VTC(Vector Transformation Coding) codebook applied with SOFM algorithm and compare the result for several coding rates with LBG algorithm. The problem of Vector quantization is complicated calculation and codebook generation. So, to solve this problem, we used neural network approach method.

• The Journal of the Acoustical Society of Korea
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• v.24 no.4E
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• pp.123-127
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• 2005

In this paper, we propose a new method for reducing the number of distance calculations in the LBG (Linde, Buzo, Gray) algorithm, which is widely used method to construct a codebook in vector quantization of speech recognition system. The proposed algorithm can reduce the distance calculation between input vector and codeword by utilizing the observation that codewords are quickly stabilized as the number of iteration increases. From the simulation results, it is shown that we can reduce the running times over $43.77\%$ on average in comparison with current LBG algorithm without sacrificing the performance of codebook.