• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.425-432
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• 2007

In this paper, we deals with automatic modulation classification capable of classifying incident signals without a priori information. The 7 key features which have good properties of sensitive with modulation types and insensitive with SNR variation are selected. The numerical simulations for classifying 9 modulation types using the these features are performed. The numerical simulations of the 4 types of modulation classifiers are performed the investigation of classification accuracy and execution time to implement the fast modulation classifier in software radio. The simulation result indicated that the execution time of DTC was best and SVC and MDC showed good classification performance. The prototype was implemented with DTC type. With the result of field trials, we confirmed the performance in the prototype was agreed with the numerical simulation result of DTC.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.4
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• pp.22-30
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• 2008

In this paper, we deal with modulation classification method using WT capable of classifying incident digital signals without a priori information. These key features should have good properties of sensitive with modulation types and insensitive with SNR variation. The 4 key features for modulation recognition are selected using WT coefficients, which have the property of insentive to the changing of noise. The numerical simulations for classifying 8 digital modulation types using these features are peformed. The numerical simulations of the 3 types (i.e. DTC, MDC, and SVMC) of modulation classifiers are performed the investigation of classification accuracy and execution time to design the modulation classification module in software radio. The simulation result indicated that the execution time of MDC and DTC was best and MDC and SVMC showed good classification performance.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.1-5
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• 2008

In this paper, we propose and analyze the AMC (Adaptive Modulation and Coding) system with efficient turbo coded V-BLAST (Vertical-Bell-lab Layered Space-Time) technique. The proposed algorithm adopts extrinsic information from a MAP (Maximum A Posteriori) decoder with iterative decoding as a priori probability in two decoding procedures of V-BLAST scheme; the ordering and the slicing. Also, we consider the AMC system using the conventional turbo coded V-BLAST technique that simply combines the V-BLAST scheme with the turbo coding scheme. And we compare the proposed decoding algorithm to a conventional V-BLAST decoding algorithm and a ML (Maximum Likelihood) decoding algorithm. In addition, we apply a STD (Selection Transmit Diversity) scheme to the systems for better performance improvement. Results indicate that the proposed systems achieve better throughput performance than the conventional systems over the entire SNR range. In terms of transmission rate performance, the suggested system is close in proximity to the conventional system using the ML decoding algorithm.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1874-1881
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• 2010

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.

• Journal of Broadcast Engineering
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• v.19 no.6
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• pp.967-970
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• 2014

An efficient peak detection algorithm in magnitude spectrum is proposed to distinguish the M-frequency shift keying(FSK) signals from other digitally modulated signal. In addition, recognition of the modulation order estimation of FSK signals is also studied based on the fact that the magnitude spectrum of FSK signals reveals the number of peaks equal to the modulation order. When no a priori information about the signals, we utilize the histogram of the magnitude spectrum to determine the threshold which is important factor in peak detection algorithm. The simulation results show high probability of classification under 500 symbols and signal-to-noise ratio(SNR) higher than 4dB.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.102-109
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• 2008

In this paper, the robust hybrid modulation type classifier which use both HOS and WT key features and can recognize 10 digitally modulated signals without a priori information in multipath fading channel conditions is proposed. The proposed classifier developed using data taken field measurements in various propagation model (i,e., rural area, small town and urban area) for real world scenarios. The 9 channel data are used for supervised training and the 6 channel data are used for testing among total 15 channel data(i.e., holdout-like method). The Proposed classifier is based on HOS key features because they are relatively robust to signal distortion in AWGN and multipath environments, and combined WT key features for classifying MQAM(M=16, 64, 256) signals which are difficult to classify without equalization scheme such as AMA(Alphabet Matched Algorithm) or MMA(Multi-modulus Algorithm. To investigate the performance of proposed classifier, these selected key features are applied in SVM(Support Vector Machine) which is known to having good capability of classifying because of mapping input space to hyperspace for margin maximization. The Pcc(Probability of correct classification) of the proposed classifier shows higher than those of classifiers using only HOS or WT key features in both training channels and testing channels. Especially, the Pccs of MQAM 3re almost perfect in various SNR levels.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.6 s.360
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• pp.40-47
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• 2007

In this paper, we propose and analyze the Adaptive Modulation System with optimal Turbo Coded V-BLAST(Vertical-Bell-lab Layered Space-Time) technique that adopts the extrinsic information from MAP (Maximum A Posteriori) Decoder with Iterative Decoding as a priori probability in two decoding procedures of V-BLAST; the ordering and the slicing. Also, we consider and compare the Adaptive Modulation System using conventional Turbo Coded V-BLAST technique that is simply combined V-BLAST with Turbo Coding scheme and the Adaptive Modulation System using conventional Turbo Coded V-BLAST technique that is decoded by the ML (Maximum Likelihood) decoding algorithm. We observe a throughput performance and a complexity. As a result of a performance comparison of each system, it has been proved that the complexity of the proposed decoding algorithm is lower than that of the ML decoding algorithm but is higher than that of the conventional V-BLAST decoding algorithm. however, we can see that the proposed system achieves a better throughput performance than the conventional system in the whole SNR (Signal to Noise Ratio) range. And the result shows that the proposed system achieves a throughput performance close to the ML decoded system. Specifically, a simulation shows that the maximum throughput improvement in each MIMO scheme is respectively about 350 kbps, 460 kbps, and 740 kbps compared to the conventional system. It is suggested that the effect of the proposed decoding algorithm accordingly gets higher as the number of system antenna increases.