• Journal of Communications and Networks
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• 제12권5호
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• pp.433-441
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• 2010

Multihop transmission is a promising technique that helps in achieving broader coverage (excellent network connectivity) and preventing the impairment of wireless channels. This paper proposes a cluster-based multihop wireless network that makes use of the advantages of multihop relaying, i.e., path loss gain, and partial relay selection in each hop, i.e., spatial diversity. In this partial relay selection, the node with the maximum instantaneous channel gain will serve as the sender for the next hop. With the proposed protocol, the transmit power and spectral efficiency can be improved over those in the case of direct transmission and conventional multihop transmission. Moreover, at a high signal-to-noise ratio (SNR), the performance of the system with at least two nodes in each cluster is dependent only on the last hop and not on any of the intermediate hops. For a practically feasible decode-and-forward relay strategy, a compact expression for the probability density function of the end-to-end SNR at the destination is derived. This expression is then used to derive closed-form expressions for the outage probability, average symbol error rate, and average bit error rate for M-ary square quadrature amplitude modulation as well as to determine the spectral efficiency of the system. In addition, the probability of SNR gain over direct transmission is investigated for different environments. The mathematical analysis is verified by various simulation results for demonstrating the accuracy of the theoretical approach.

• The Journal of the Acoustical Society of Korea
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• 제18권1E호
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• pp.3-6
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• 1999

In digital mobile communication system, the transmission errors affect the quality of output speech seriously. There are many error concealment techniques using a posteriori probability which provides information about any transmitted parameter. They need knowledge about channel transition probability as well as the 1st order Markov transition probability of codec parameters for estimation of transmitted parameters. However, in applications of mobile communication systems, the channel transition probability varies depending on nonstationary channel characteristics. The mismatch of designed channel transition probability of the estimator to actual channel transition probability degrades the performance of the estimator. In this paper, we proposed a new parameter estimator which adapts to the channel characteristics using short time average of maximum a posteriori probabilities(MAPs). The proposed scheme, when applied to the LSP parameter estimation, performed better than the conventional estimator which do not adapt to the channel characteristics.

• 한국통신학회논문지
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• 제37권6A호
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• pp.438-449
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• 2012

본 논문에서는 다양한 CRC 코드의 성능분석을 기반으로 새로운 VHF 대역 해양 무선통신용 최적 CRC-p 코드를 제안한다. 이를 위해, 먼저 CRC 코드의 부호어 길이의 변화에 따른 미검출 오류확률과 최소해밍거리를 구하는 방법을 기술한다. 즉 순회 해밍코드나 원시 BCH 코드의 쌍대코드가 최대장 코드가 되는 것을 이용해서 천이 레지스터에 의한 간단한 회로구성으로 무게분포와 미검출 오류확률을 계산하는 방법과 MacWilliam의 항등식에 의한 최소해밍거리를 계산하는 방법을 제시한다. 다음으로 VHF 대역 해양 무선통신 시스템의 전송 프레임의 구성과 주요 통신 파라미터의 규격을 제시하고, 기존의 연구된 다양한 CRC 코드의 생성다항식을 대상으로 미검출 오류확률과 최소해밍거리의 결과를 기반으로 새로운 CRC-p 코드를 선정하고, 라이시안 해양 채널모델과 ${\pi}$/4-DQPSK 변복조기에 의한 비트오류율(BER)의 모의실험 결과를 통해 성능을 검증한다.

• 한국통신학회논문지
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• 제8권2호
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• pp.76-81
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• 1983

PCM符號가 NRZ-L인 binary data를 5次 Bessel low-pass filter를 通하여 傳送하는 telemetry systemn에 對하여 FM方式으로 RF link를 構成하고 liniter-discriminator로 複調하였을 경우에 digital通信system의 性能評價에 重要한 bit誤差確率을 SNR에 對하여 解析的으로 檢討하였다. 實際로 設計에 적용한 telemetry system에서 bit rate를 140kHz, 變調前 filter의 f를 100kHz, 送信機의 最大周波數編移 2f를 300kHz로 設計하였을 때 f0T=0.7 및 h=2ft=2이며 SNR이 10dB일 때 bit誤差確率은 10으로 計算되었다.

• 전자공학회논문지B
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• 제28B권10호
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• pp.831-841
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• 1991

A combined model of probabilistic and MLP(multi layer perceptron) model is proposed for the pattern classification of EMG( electromyogram) signals. The MLP model has a problem of not guaranteeing the global minima of error and different quality of approximations to Bayesian probabilities. The probabilistic model is, however, closely related to the estimation error of model parameters and the fidelity of assumptions. A proper combination of these will reduce the effects of the problems and be robust to input variations. Proposed model is able to get the MAP(maximum a posteriori probability) in the probabilistic model by estimating a priori probability distribution using the MLP model adaptively. This method minimize the error probability of the probabilistic model as long as the realization of the MLP model is optimal, and this is a good combination of the probabilistic model and the MLP model for the usage of MLP model reliability. Simulation results show the benefit of the proposed model compared to use the Mlp and the probabilistic model seperately and the average calculation time fro classification is about 50ms in the case of combined motion using an IBM PC 25 MHz 386model.

• 전자공학회논문지B
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• 제31B권12호
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• pp.108-115
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• 1994

Most pattern classifiers have been designed based on the ML (Maximum Likelihood) training algorithm which is simple and relatively powerful. The ML training is an efficient algorithm to individually estimate the model parameters of each class under the assumption that all class models in a classifier are statistically independent. That assumption, however, is not valid in many real situations, which degrades the performance of the classifier. In this paper, we propose a minimum-error-rate training algorithm based on the MAP (Maximum a Posteriori) approach. The algorithm regards the normalized outputs of the classifier as estimates of the a posteriori probability, and tries to maximize those estimates. According to Bayes decision theory, the proposed algorithm satisfies the condition of minimum-error-rate classificatin. We apply this algorithm to NPM (Neural Prediction Model) for speech recognition, and derive new disrminative training algorithms. Experimental results on ten Korean digits recognition have shown the reduction of 37.5% of the number of recognition errors.

• 전자공학회논문지B
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• 제30B권12호
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• pp.1-11
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• 1993

This paper concentrates on models useful for analyzing the error performance of ML(Maximum Likelihood) estimators of a single unknown signal parameter: that is the error intensity model. We first develop the point process representation for the estimation error and the conditional distribution of the estimator as well as the distribution of error candidate point process. Then the error intensity function is defined as the probability dessity of the estimate and the general form of the error intensity function is derived. We then develop several intensity models depending on the way we choose the candidate error locations. For each case, we compute the explicit form of the intensity function and discuss the trade-off among models as well as the extendability to the case of multiple parameter estimation.

• Communications for Statistical Applications and Methods
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• 제27권1호
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• pp.47-64
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• 2020

In this paper, we derive some estimators of the scale parameter of the exponentiated half-logistic distribution based on the multiply Type-I hybrid censoring scheme. We assume that the shape parameter λ is known. We obtain the maximum likelihood estimator of the scale parameter σ. The scale parameter is estimated by approximating the given likelihood function using two different Taylor series expansions since the likelihood equation is not explicitly solved. We also obtain Bayes estimators using prior distribution. To obtain the Bayes estimators, we use the squared error loss function and general entropy loss function (shape parameter q = -0.5, 1.0). We also derive interval estimation such as the asymptotic confidence interval, the credible interval, and the highest posterior density interval. Finally, we compare the proposed estimators in the sense of the mean squared error through Monte Carlo simulation. The average length of 95% intervals and the corresponding coverage probability are also obtained.

• Journal of the Korean Data and Information Science Society
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• 제8권1호
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• pp.91-97
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• 1997

Maximum likelihood and jackknife estimators of the location and scale parameters and right-tail probability in the truncated arcsine distribution are proposed, and we shall compare the performances of the proposed estimators in terms of bias and mean squared error.

• 인터넷정보학회논문지
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• 제11권5호
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• pp.1-8
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• 2010

이 논문은 충격성 잡음 환경에 대해 상수 모듈러스 오차(CME)와 가우시안 커널에 근거한 블라인드 등화 알고리듬의 성능 분석을 보이고 있다. CME와 평균 자승 오차(MSE)에 근거한 상수 모듈러스 알고리듬(CMA)는 충격성 잡음 환경에서 수렴에 실패한다. 이런 충격성 잡음에 대한 내항성을 위해 최근에 소개된 코렌트로피 블라인드 등화 알고리듬도 PAM 변조 방식에서는 만족할 만한 결과를 보이지 못한다. 원래 가우시안 잡음 환경을 위해 제안되었던 최대 영-확률 블라인드 알고리듬(MZEP-CME)이 충격성 잡음 환경에서도 탁월한 성능을 보인다는 것이 이 논문의 이론적, 그리고 시뮬레이션을 통한 분석에 의해 입증된다. MZEP-CME 알고리듬의 가우시안 커널은 충격성 잡음에 의해 발생하는 출력 신호 전력과 CME 사이의 큰 차이에 민감하게 반응하지 못하게 하는 강한 영향력을 발휘한다.