• Journal of Communications and Networks
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• v.13 no.1
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• pp.63-69
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• 2011

In this paper, we propose a fixed-complexity sphere encoder (FSE) for multi-user multi-input multi-output (MU-MIMO) systems. The proposed FSE accomplishes a scalable tradeoff between performance and complexity. Also, because it has a parallel tree-search structure, the proposed encoder can be easily pipelined, leading to a tremendous reduction in the precoding latency. The complexity of the proposed encoder is also analyzed, and we propose two techniques that reduce it. Simulation and analytical results demonstrate that in a $4{\times}4$ MU-MIMO system, the proposed FSE requires only 11.5% of the computational complexity needed by the conventional QR decomposition with M-algorithm encoder (QRDM-E). Also, the encoding throughput of the proposed encoder is 7.5 times that of the QRDM-E with tolerable degradation in the BER performance, while achieving the optimum diversity order.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.4
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• pp.590-597
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• 1990

To represent shape characteristics of digital closed curve, many algorithms, mainly based on local properties, have been proposed. In this paper, we propose a new algorithm for detecting local curvature maxima which reflects context, i.e., structural or surrounding regional characteristics. The algorithm does not require the value of k as an input parameter which is the major problem in k-curvature method in digital curve, but calculates it at each point depening on the context. The algorithm has been applied to two dimensional image boundaries. The efficiency of the algorithm is addressed by comparing the result of existing contest dependent algorithm.

• ETRI Journal
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• v.28 no.2
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• pp.155-161
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• 2006

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.

• Journal of Electrical Engineering and information Science
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• v.2 no.5
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• pp.1-8
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• 1997

The main purpose of the Boolean equivalence problem is to verify that two Boolean expressions have the same functionality. Simulation has been extensively used as the standard method for the equivalence problem. Obviously, the number of tests required to perform a satisfactory coverage grows exponentially with the number of input variables. However, formal methods as opposed to simulation are getting more attention from the community. We propose a new algorithm called the Cover-Merge Algorithm based on a derivational method using the concept of cover and merge for the equivalence problem and investigate its theoretical aspects. Because of the difficulty of the problem, we emphasize simplification techniques in order to reduce the search space or problem size. Heuristics based on types of merges are developed to speed up the derivation process by allowing simplifications. In comparison with widely used technique called Binary Decision Diagram or BDD, the algorithm proposed outperforms BDD in nearly all cases of input including standard benchmark problems.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.41 no.9
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• pp.65-71
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• 2004

This paper describes a new adaptive algorithm which can reduce the required computation quantities in the adaptive filter. The proposed S-LMS algorithm uses only the signs of the normalized input signal rather than the input signals when coefficients of the filter are adapted. By doing so, there is no need for the multiplications and divisions which are mostly responsible for the computation quantities. To analyze the convergence characteristics of the proposed algorithm, the condition and speed of the convergence are derived mathematically. Also, we simulate an echo canceller adopting this algorithm and compare the performances of convergence for this algorithm with the ones for the other algorithm. As the results of simulations, it is proved that the echo canceller adopting this algorithm shows almost the same performances of convergence as the echo canceller adopting the SIA algorithm.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.4
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• pp.324-341
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• 2023

Topological data analysis (TDA) is a data analysis technique, recently developed, that investigates the overall shape of a given dataset. The mapper algorithm is a TDA method that considers the connectivity of the given data and converts the data into a mapper graph. Compared to persistent homology, another popular TDA tool, that mainly focuses on the homological structure of the given data, the mapper algorithm is more of a visualization method that represents the given data as a graph in a lower dimension. As it visualizes the overall data connectivity, it could be used as a prediction method that visualizes the new input points on the mapper graph. The existing mapper packages such as Giotto-TDA, Gudhi and Kepler Mapper provide the descriptive mapper algorithm, that is, the final output of those packages is mainly the mapper graph. In this paper, we develop a simple predictive algorithm. That is, the proposed algorithm identifies the node information within the established mapper graph associated with the new emerging data point. By checking the feature of the detected nodes, such as the anomality of the identified nodes, we can determine the feature of the new input data point. As an example, we employ the fraud credit card transaction data and provide an example that shows how the developed algorithm can be used as a node prediction method.

• Journal of IKEEE
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• v.13 no.1
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• pp.1-10
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• 2009

Recently, some frequency domain CMA (Constant Modulus Algorithm) have been introduced in an effort to reduce computational complexities [1,2]. In [1], a fast algorithm that minimizing cost function designed for block input signal is employed, while in [2], a novel cost function that minimizing sample by sample input is used. Although, the two fast algorithm save computational complexities as compared to CMA, the convergence behaviors of the two fast algorithm show different results with repsect to CMA. Thus, in this paper, some analytical results on the error surface of the fast frequency domain CMA are introduced. From the analytical results, we show that the more recent algorithm [2] outperforms the previous algorithm [1]. Simulation results reveals that the recent algorithm [2] shows 50% enhanced convergence with respect to the old fast algorithm [1]. Also, we show that the recent fast algorithm [2] has comparable convergence performance with respect to conventional CMA algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2120-2133
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• 2013

In this paper, we address the problem of linear minimum mean-squared error (MMSE) transmitter design for the cognitive radio (CR) multi-user multiple-input single-output (MU-MISO) broadcasting channel (BC), where the cognitive users are subject to not only a sum power constraint, but also a interference power constraint. Evidently, this multi-constraint problem renders it difficult to solve. To overcome this difficulty, we firstly transform it into its equivalent formulation with a single constraint. Then by utilizing BC-MAC duality, the problem of BC transmitter design can be solved by focusing on a dual MAC problem, which is easier to deal with due to its convexity property. Finally we propose an efficient two-level iterative algorithm to search the optimal solution. Our simulation results are provided to corroborate the effectiveness of the proposed algorithm and show that this proposed CR MMSE-based scheme achieves a suboptimal sum-rate performance compared to the optimal DPC-based algorithm with less computational complexity.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.436-444
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• 2010

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.4C
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• pp.384-391
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• 2003

Blind channel identification and equalization attempt to identify the communication channel and to remove the inter-symbol interference caused by a communication channel without using any known trainning sequences. In this paper, we propose a blind adaptive channel identification and equalization algorithm with phase offset compensation for single-input multiple-output (SIMO) channel. It is based on the one-step forward multichannel linear prediction error method and can be implemented by an RLS algorithm. Phase offset problem, we use a blind adaptive algorithm called the constant modulus derotator (CMD) algorithm based on condtant modulus algorithm (CMA). Moreover, unlike many known subspace (SS) methods or cross relation (CR) methods, our proposed algorithms do not require channel order estimation. Therefore, our algorithms are robust to channel order mismatch.