• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.1
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• pp.140-142
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• 2013

Cognitive radio (CR) has been recently proposed to dynamically access unused-spectrum. Since the spectrum availability for opportunistic access is determined by spectrum sensing, sensing control is identified as one of the most crucial issues of cognitive radio networks. Out-of-band sensing to find an available channels to sense. Sensing is also required in case of spectrum hand-off. Sensing process needs to be done very fast in order to enhance the quality of service (QoS) of the CR nodes, and transmission not to be cut for longer time. During the sensing, the PU(primary user) detection probability condition should be satisfied. We adopt a channel prediction method to find target channels. Proposed prediction method combines chaotic global method and chaotic local method for channel idle probability prediction. Global method focus on channel history information length and order number of prediction model. Local method focus on local prediction trend. Through making simulation, Proposed method can find an available channel with very high probability, total sensing time is minimized, detection probability of PU's are satisfied.

• 한국정보통신설비학회:학술대회논문집
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• 2008.08a
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• pp.116-120
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• 2008

The conventional channel selection method for a ZigBee communication network basically detects the energy values in all channels. In the ISM band, no license is required to use channels in this band, so there may exist various interference factors in this band. It is well known that WLAN is the major interference factor degrading the performance of ZigBee in the ISM band. In this paper, we propose an efficient channel selection algorithm which uses the pattern of WLAN channel uses as the a priori information. By using the proposed algorithm, we may save the time required to select channels for the ZigBee communications.

• Culinary science and hospitality research
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• v.24 no.3
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• pp.204-214
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• 2018

The study set up research models and hypotheses to examine the influence of HMR selection attributes on satisfaction and repurchase intention by distribution channels(convenience store/large market), verify the research hypothesis through empirical analysis, respectively. The purpose of this study is to investigate the convenience sampling method of HMR purchase from convenience store and large market in Seoul and Gyeonggi area. The survey was conducted from January 8, 2018 to January 26, 2018, and 300 questionnaires were distributed and 289 of them were used as an effective data. For the empirical analysis, SPSS 20.0 was used. The results of the analysis are as follows. First, product quality only has a significant effect on satisfaction among HMR selection attributes at convenience store, and product safety and convenience have no significant effect on satisfaction. Second, only the convenience of HMR selection attributes in the large market has a significant effect on satisfaction, and product safety and product quality have no significant effect on satisfaction. Third, HMR satisfaction in convenience stores and large markets has a significant effect on repurchase intention. The purpose of this study is to investigate the relationships among HMR selection attributes, satisfaction, and repurchase intention, which are important in the existing HMR research, by each distribution channel(convenience store/large market). It is meaningful to help them establish an effective sales strategy for each segment.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1422-1428
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• 2009

In this paper, we consider an independent type cognitive radio system where secondary users can utilize empty channels that are not currently used by primary users having the license to these channels. In the previous works, secondary users search channels sequently or randomly to detect activities of primary user on channels. These channel searching methods however are not suitable to the characteristics of the wireless environment. Therefore, we propose a channel searching method based on the channel list for the purpose of reducing the channel searching time and improving the throughput of secondary users. In the proposed method, we firstly determine weighting value of each channel based on the history of channel activities of primary users and add the weighing value to current channel state buffer. And then, we search an empty channel from channel with smallest value to one with the biggest value. Finally, we compare the performances of the proposed method with those of the sequential channel searching and the random channel searching methods in terms of average channel searching time and average number of transmissions of secondary user.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.1C
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• pp.22-27
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• 2011

This paper proposes an efficient user selection algorithm that provides a maximum 셀 용량 based Multi-user MIMO system based on zero-forcing criterion. The proposed scheme forms a primary group of users whose channel power exceeds a predetermined threshold. Through computer simulations, we have found that the proposed method outperforms the conventional scheme yielding a sum rate that is 0.33 bps/Hz higher when the total numbers of users and transmit antennas in the cell are 100 and 4, respectively.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.3
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• pp.77-85
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• 2018

Multi-User Multiple-Input Multiple-Output (MU-MIMO) is the core technology for improving the channel capacity compared to Single-User MIMO (SU-MIMO) by using multiuser gain and spatial diversity. Key problem for the MU-MIMO is the user selection which is the grouping the users optimally. To solve this problem, we adopt Extreme Value Theory (EVT) at the beginning of the proposed algorithm, which defines a primary user set instead of a single user that has maximum channel power according to a predetermined threshold. Each user in the primary set is then paired with all of the users in the system to define user groups. By comparing these user groups, the group that produces a maximum sum rate can be determined. Through computer simulations, we have found that the proposed method outperforms the conventional technique yielding a sum rate that is 0.81 bps/Hz higher when the transmit signal to noise ratio (SNR) is 30 dB and the total number of users is 100.

• Journal of Communications and Networks
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• v.13 no.4
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• pp.377-384
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• 2011

Cooperative beamforming has previously been proven to be an efficient way to improve the cooperative diversity. This method generally requires all relay nodes to participate in beamforming, which can be seen as "all participate" cooperative beamforming. However, not all relay nodes have constructive impacts on the end-to-end bit error rate (BER) performance. Based on this observation, we propose a new cooperative scheme which only selects those "appropriate" relay nodes to perform cooperative beamforming. Such relay nodes can be simply determined with mean channel gains. Therefore, the selection complexity is significantly reduced as global instantaneous channel state information is not required. This scheme guarantees that energy is only allocated to the "appropriate" relay nodes, and hence provides superior diversity. We also prove that power allocation among source and selected relay nodes is a convex problem, and can be resolved with lower computational complexity. Simulation results demonstrate that our scheme achieves an essential improvement in terms of BER performance for both optimal and limited feedback scenarios, as well as high energy-efficiency for the energy-constrained networks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.5C
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• pp.398-405
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• 2005

The multiuser detection (MUD) based wireless receiver requires more accurate channel estimation than the single user detection (SUD) schemes such as Rake receiver, and hence the post processing is required for MUD to clean up the estimated channel coefficients by eliminating the noise only coefficients. The adaptive post processing method is proposed in order to provide more accurate channel responses and the power level of the background noise and interferences at the cost of the negligible processing delay compared to the conventional method based on the threshold test with the threshold value relative to the noise variance. The simulations are performed in 3GPP-TDD mode environment. The results show that the noise estimation error of the proposed method is maximum $10\%$, which is much smaller than $50\%$ maximum error of the conventional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.10A
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• pp.943-951
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• 2006

Multi-input-multi-output (MIMO) systems are considered to improve the capacity and reliability of next generation mobile communication. However, the multiple RF chains associated with multiple antennas are costly in terms of size, power and hardware. Antenna selection is a low-cost low-complexity alternative to capture many of the advantages of MIMO systems. We proposed new joint Tx/Rx antenna selection algorithm with low complexity. The proposed algorithm is a method selects $L_R{\times}L_T$ channel matrix out of $L_R{\times}L_T$ entire channel gain matrix where $L_R{\times}L_T$ matrix selects alternate Tx antenna with Rx antenna which have the largest channel gain to maximize Frobenius norm. The feature of this algorithm is very low complexity compare with Exhaustive search which have optimum capacity. In case of $4{\times}4$ antennas selection out of $8{\times}8$ antennas, the capacity decreases $0.5{\sim}2dB$ but the complexity also decreases about 1/10,000 than optimum exhaustive search.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.774-779
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• 2012

Brain-computer interface based on motor imagery is a system that transforms a subject's intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject's limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).