• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -2
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• pp.1354-1357
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• 2002

In this paper, we propose a suboptimal receiver combining adaptive array antenna and orthogonal decision-feedback detector in DS/CDMA system. Adaptive array antenna can cancel out undesired signal using beamforming scheme. However, if there are interfering signals from undesired users with the same incident angle as that of a desired user, an adaptive array antenna cannot suppress them. The proposed receiver can cancel out remaining interference from users having nearly the same beam pattern. And we employ Orthogonal Decision-Feedback Detector (ODFD) as multiuser detection. The ODFD performs as good as the decorrelating decision -feedback detector (DDD) with much less complexity. Simulation results show that the proposed system provides a significantly enhanced performance.

• 전기학회논문지
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• 제56권11호
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• pp.2033-2035
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• 2007

This paper proposes a novel iterative LMS-based decision feedback equalizer for short burst transmission with relatively short training sequence. In the proposed equalizer, the longer concatenated training sequence can provide the more sufficient channel information and the reused original training sequence can provide the correct decision feedback information. In addition, the overall adaptive processing is performed using the low complexity LMS algorithm. The study shows the performance of the proposed method is enhanced with the number of iterations and, furthermore, better than that of the conventional LMS-based DFEs with the training sequence of longer or equal length. Computational complexity is increased linearly with the number of iterations.

• Journal of Communications and Networks
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• 제5권1호
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• pp.7-18
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• 2003

In this paper, a decision feedback recurrent neural network equalizer and a modified real time recurrent learning algorithm are proposed, and an adaptive adjusting of the learning step is also brought forward. Then, a complex case is considered. A decision feedback complex recurrent neural network equalizer and a modified complex real time recurrent learning algorithm are proposed. Moreover, weights of decision feedback recurrent neural network equalizer under burst-interference conditions are analyzed, and two anti-burst-interference algorithms to prevent equalizer from out of working are presented, which are applied to both real and complex cases. The performance of the recurrent neural network equalizer is analyzed based on numerical results.

• 한국전자파학회논문지
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• 제18권11호
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• pp.1237-1242
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• 2007

이 논문에서는 시변 레일리 페이딩 채널에서 STBC(Space Time Block Code)의 신호 검출 방법을 연구한다. 하나의 블록 내의 두 심볼 구간 동안 채널 환경이 변하는 경우, 채널 행렬의 직교성이 성립하지 않고 두 심볼간에 간섭이 발생한다. 이러한 채널에서 수신 성능을 개선하기 위해서 joint ML 검출 방식이 제안되었으나, 계산복잡도가 증가한다. 이를 줄이기 위해서 판정 궤환(decision feedback) 방식이 제안되었으나, joint ML 방식에 비해 수신 성능이 떨어진다. 이에 이 논문에서는 joint ML 검출 방법을 사용하는 경우에 비해 수신기의 복잡도를 줄이는 동시에 기존의 판정 궤환 방식에 비해 성능이 개선된 이중 판정 궤환 방식을 이용한 새로운 STBC 검출 알고리즘을 제안한다.

• 한국전자파학회논문지
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• 제21권9호
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• pp.995-1004
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• 2010

채널의 상태가 변하는 전송 환경에서 수신된 신호에 대한 잡음비를 추정하여, 보다 효율적으로 신호를 전송하는 것은 현대 통신 시스템에서 중요한 기술이다. 기존 NDA(Non-Data-Aided) SNR 추정 방법은 M진 APSK 또는 같은 고차원 신호의 SNR 추정 성능이 떨어진다. 본 논문에서는 OFDM 시스템에서 블록 단위 수신 신호의 영점 자기 상관과 decision feedback 신호의 자기 상관 및 상호 상관을 이용하는 SNR 추정 방법을 제안한다. 본 논문에서 제안한 방법은 decision feedback 신호의 2차 모멘트인 영점 자기 상관을 이용하여 SNR을 추정하는 Type 1 방식과 4차 모멘트 성질을 갖고 있는 영점 자기 상관과 상호 상관을 이용한 Type 2 방식이다. 이 두가지 SNR 추정 방식은 OFDM 시스템에서 블록 단위 수신을 할 때, 신호의 상관 관계에 기반을 두고 있어 SNR 추정 방법의 실용적인 구현이 가능하게 하고, decision feedback 신호를 사용함으로써 QAM 신호에서도 종전의 SNR 추정 방식들보다 비교적 안정적인 추정 성능을 보인다. 또한, decision feedback 신호를 사용할 때 자기 상관과 상호 상관의 오차에 따른 SNR 추정 식을 수식적으로 유도한다. 그리고 Monte Carlo 시뮬레이션을 통해 제안한 SNR 추정 방법의 성능을 확인한다.

• Asia pacific journal of information systems
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• 제5권1호
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• pp.164-185
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• 1995

A considerable number of empirical studies have been conducted into the impact of graphical presentation of information on decision making performance. Very limited attention has been, however, devoted to the role of graphs as a means of delivering feedback. This paper investigated this issue by varying the presentation formats of outcome feedback in time series forecasting contexts. It was found that overall feedback was not decision-effective. Even enlarged and more salient feedback was of little value to overcome the conservative behavior. The results doubted the efficacy of graphical feedback in the design of EIS/DSS. Feedback did not appear to be a simple mechanism and further research is required to investigate its cognitive processes.

• 산업경영시스템학회지
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• 제46권3호
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• pp.109-122
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• 2023

As platforms become primary decision making tools, platforms for decision have been introduced to improve quality of decision results. Because, decision platforms applied augmented decision-making process which uses experiences and feedback of users. This process creates a variety of alternatives tailored for users' abilities and characteristics. However, platform users choose alternatives before considering significant quality factors based on securing decision quality. In real world, platform managers use an algorithm that distorts appropriate alternatives for their commercial benefits. For improving quality of decision-making, preceding researches approach trying to increase rational decision -making ability based on experiences and feedback. In order to overcome bounded rationality, users interact with the machine to approach the optional situation. Differentiated from previous studies, our study focused more on characteristics of users while they use decision platforms. This study investigated the impact of quality factors on decision-making using platforms, the dimensions of systematic factors and user characteristics. Systematic factors such as platform reliability, data quality, and user characteristics such as user abilities and biases were selected, and measuring variables which trust, satisfaction, and loyalty of decision platforms were selected. Based on these quality factors, a structural equation research model was created. A survey was conducted with 391 participants using a 7-point Likert scale. The hypothesis that quality factors affect trust was proved to be valid through path analysis of the structural equation model. The key findings indicate that platform reliability, data quality, user abilities, and biases affect the trust, satisfaction and loyalty. Among the quality factors, group bias of users affects significantly trust of decision platforms. We suggest that quality factors of decision platform consist of experience-based and feedback-based decision-making with the platform's network effect. Through this study, the theories of decision-making are empirically tested and the academic scope of platform-based decision-making has been further developed.

• Journal of information and communication convergence engineering
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• 제4권2호
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• pp.58-61
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• 2006

A key issue toward mobile multimedia communications is to create technologies for broadband signal transmission that can support high quality services. Such a broadband mobile communications system should be able to overcome severe distortion caused by timevarying multi-path fading channel, while providing high spectral efficiency and low power consumption. For these reasons, an adaptive suboptimum decision feedback equalizer (DFE) for the single-carrier shortburst transmissions system is considered as one of the feasible solutions. For the performance improvement of the system with the short-burst format including the short training sequence, in this paper, the multiple-training least mean square (MTLMS) based DFE scheme with soft decision feedback is proposed and its performance is investigated in mobile wireless channels throughout computer simulation.

• Journal of Electrical Engineering and Technology
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• 제12권3호
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• pp.1280-1285
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• 2017

In this paper, we consider iterative soft-decision feedback equalizers (sDFE), a.k.a. turbo equalizers for single-carrier transmission. Turbo equalizer takes log-likelihood ratio (LLR) feedback from channel decoder and convert the LLR into symbol estimates and variances to be used for the LLR update at the sDFE. Specifically, we consider both time domain and frequency-domain sDFE and compare their performances. The results shows that frequency-domain sDFE performs better than time-domain one and also that considerable gain can be obtained especially when the channel has deep nulls.

• Journal of Information Processing Systems
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• 제13권1호
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• pp.8-25
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• 2017

This survey paper explores the application of multimodal feedback in automated systems for motor learning. In this paper, we review the findings shown in recent studies in this field using rehabilitation and various motor training scenarios as context. We discuss popular feedback delivery and sensing mechanisms for motion capture and processing in terms of requirements, benefits, and limitations. The selection of modalities is presented via our having reviewed the best-practice approaches for each modality relative to motor task complexity with example implementations in recent work. We summarize the advantages and disadvantages of several approaches for integrating modalities in terms of fusion and frequency of feedback during motor tasks. Finally, we review the limitations of perceptual bandwidth and provide an evaluation of the information transfer for each modality.