• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.982-987
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• 2012

In this paper, the SNR estimation error of Decision Directed SNR estimation method in AWGN is investigated, which uses samples received in reference decision region. In communication system receiver, when SNR estimation scheme using error vectors between ideal sample points and received sample points of reference region is adopted, the samples contain incorrectly received samples due to AWGN. Consequently, the mean of estimated reference constellation point is shifted and Decision Directed SNR estimation is inaccurately performed. These effects are explained by modified probability density function and difference between actual SNR and estimated SNR is theoretically derived and quantatively analyzed. It is proved that SNR estimation error obtained through computer simulation is matched up with derived one, and SNR estimation performance is enhanced significantly by adopting suggested correction scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.3
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• pp.419-424
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• 2015

The Decision-directed (DD) algorithm is known to be not effective to initialize a blind equalizer in the channel conditions when the eye diagram of received signals is completely closed because it can not open the eye diagram enough. In this paper, we propose a new error to replace the error of the conventional DD algorithm. The new DD error is the conventional DD error scaled by the modulus of symbol decision, new DD algorithm using this error is effective to open the closed eye diagram in early stage of equalization unlike the conventional DD. The new DD algorithm appling the new error is showed excellent convergence characteristics as compared to the CMA widely used in blind initialization, particularly, is useful for equalization of signals having multimodulus. The performance of the new DD algorithm is verified through the simulation for the higher-order QAM signals.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.3
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• pp.73-80
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• 2010

Complex-valued blind algorithms which are based on constant modulus error and Euclidian distance (ED) between two probability density functions show relatively poor performance in spite of the advantages of information theoretic learning since the inherent characteristics of the constant modulus error prevent the algorithm from coping with the symbol phase rotation caused by the complex channels. In this paper, we show that the symbol phase rotation problem can be avoided and the advantages of information theoretic learning can be preserved by introducing decision-directed mode to the blind algorithm whenever the equalizer output power lies in the neighborhood of multi-modulus levels. Simulation results through MSE convergence and constellation comparison for severely distorted complex channels show significantly enhanced performance of symbol-point concentration and no phase rotation problems caused by the complex channel models.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.2
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• pp.78-85
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• 2002

We introduce a rotational decision-directed joint algorithm of blind equalization coupled with carrier recovery for 32-QAM demodulation with high symbol rate. The proposed carrier recovery, which we call a rotational decision-directed carrier recovery(RDDCR), removes the residual phase difference by rotating the decision boundary for the kth received symbol by the frequency detector output of the (k-1)th received symbol. Since the RDDCR includes the function of PLL loop filter by rotating the decision boundary, it gives a simpler demodulator structure. The rotational decision-directed blind equalization(RDDBE) with the rotated decision boundary based on the Stop-and-Go Algorithm(SGA) operated during tracking the frequency offset by the RDDCR and removes intersymbol interference due to multipaths and channel noise. Test results show that symbol error rate of $10^{-3}$ is obtained before the forward error correction when SNR equals 15dB with 150KHz of carrier frequency offset and two multipaths, which is the channel condition for 32-QAM receiver.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.3-8
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• 2016

We propose a modified form of the decision-directed least mean square (DD LMS) algorithm that is widely used in the optimization of self-adaptive equalizers, and show the modified version greatly improves the initial convergence properties of the conventional algorithm. Existing DD LMS regards the difference between a equalizer output and a quantization value for it as an error, and achieves an optimization of the equalizer based on minimizing the mean squared error cost function for the equalizer coefficients. This error generating method is useful for binary signal or a single-level signals, however, in the case of multi-level signals, it is not effective in the initialization of the equalizer. The modified DD LMS solves this problem by modifying the error generation. We verified the usefulness and performance of the modified DD LMS through experiments with multi-level signals under distortions due to intersymbol interference and additive noise.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.11
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• pp.2451-2458
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• 1997

Adaptive channel equalization complished without resorting to a training sequence is known as blind equalization. In this paper, in order to increase the speed of the convergence and to reduce the steady-state mean squared error simulatneously, we propose the multi-stage DD(decision-direct) algorithm derived from the combination of the Sato algorithm and the decision-directed algorithm. In the starting stage, the multi-stage DD algorithm is identical to the Sato algorithm which guarantees the convergence of the equalizer. As the blind equalizer converges, the number of the level of the quantizers is increased gradally, so that the proposed algorithm operates identical to the decision-directed algorithm which leads to the low error power after the convergence. Therefore, the multi-stage DD algorithm obtains fast convergence rate and low steady state mean squared error.

• Proceedings of the KIEE Conference
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• 1998.11b
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• pp.624-626
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• 1998

Adaptive channel equalization accomplished without resorting to a training sequence is known as blind equalization. In this paper, we present a auto-switching blind, equalization for 8-VSB HDTV receiver. The scheme operate in two mode : blind equalization mode and decision-directed equalization mode. This proposed scheme changes from the blind equalization mode at high error levels to the decision-directed equalization mode at lower error levels smoothly and automatically. Manual switch from the blind equalization mode to the decision-directed mode is not necessary.

• The Journal of Information Technology
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• v.2 no.1
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• pp.103-113
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• 1999

In this paper, we propose th soft decision-directed sto-and-go algorithm combining a concept of the stop-and-go algorithm with soft decision-directed algorithm. The proposed algorithm has an enhanced equalization performance according to using the more confidential error signal than two algorithms. By computer simulation, it is confirmed that the proposed algorithm has the performance superiority in terms of residual ISI and convergence speed compared with the adaptive blind equalization algorithm of CMA, Modified CMA(MCMA), Stop-and Go algorithm and simplified 50ft decision-directed algorithm.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.8
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• pp.1037-1045
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• 1999

We propose two new classes of robust blind equalization algorithms against abrupt changes of channel conditions, which we call a triple-mode algorithm(TMA) and an automatic switch-over algorithm(ASA). The conventional DMGSA exhibits slow convergence rates due to the incorrect equalizer tap-updating process under the severe channel conditions. In order to speed up the convergence process, the TMA operates in triple-mode that is based on the dual-mode of the DMGSA incorporated with the tap-updating control modes of the SGA as well as the MSGA. Without resorting to the decision region for selecting the operation mode in the TMA, the ASA automatically switches the blind mode to the smoother conventional decision-directed mode. The ASA uses the error functional that is the weighted sum of the Generalized Sato error and the decision-directed error, where the weights correspond to the channel conditions. Test results on 16-QAM and 8-VSB datas confirm that the TMA and the ASA perform well under the sudden changes of channel conditions.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.5
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• pp.27-33
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• 2011

In digital communication system that the Modified Constant Modulus Algorithm (MCMA) reduced the use of the adaptive equalization algorithm to combat the Inter-symbol Interference (ISI). MCMA is relatively brief operation. The major point of MCMA that it only achieves moderate convergence rate and steady state mean square error (MSE). In this paper suggest, MCMA equalization improve the performance with parallel structure. It combines Modified Constant Modulus Algorithm(MCMA) and Modified Decision Directed(MDD) algorithm. By exploiting the inherent structural relationship between the 4-QAM signal's coordinates and 16-QAM signal's coordinates, another style of cost function for Modified Constant Modulus Algorithm(MCMA) is defined and If it happen to offset of received signals and MCMA is poor performance in order to overcome this because the paper combines apply for MCMA and MDD(Modified Decision Direct) algorithm. By computer simulation, we confirmed that the proposed PMCMA-MDD algorithm has the fater convergence rate and steady mean square error than the conventional MCMA.