• Journal of Internet Computing and Services
• /
• v.11 no.5
• /
• pp.19-26
• /
• 2010

The criterionmaximizing cross-correntropy (MCC) of two different random variables has yielded superior performance comparing to mean squared error criterion. In this paper we present a complex-valued blind equalizer algorithm for QAM and complex channel environments based on cross-correntropy criterion which uses, as two variables, equalizer output PDF and Parzen PDF estimate of a self-generated symbol set. Simulation results show significantly enhanced performance of symbol-point concentration with no phase rotation in complex-channel communication.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.35C no.1
• /
• pp.42-54
• /
• 1998

For decreasing intersymbol interference (ISI) due to band-limited channels in digitalcommunication, the uses of equalization techniques are necessary. Among the useful adaptive equalization techniques, because of their ease of implementation and nonlinear capabilites, the neural networks have been used as an alternative for effectively dealing with the channel distortion. In this paepr, a complex-valued multilayer percepron is proposed as a nonlinear adaptive equalizer. After the important properties that a suitable complex-valued activation function must possess are discussed, a new complex-valued activation function is developed for the proposed schemes to deal with M-ary QAM signals of any constellation sizes. It has been further proven that by the nonlinear transformation of the proposed function, the correlation coefficient between the real and imaginary parts of input data decreases when they are jointly Gaussian random variables. Lastly, the effectiveness of the proposed scheme is demonstrated by simulations. The proposed scheme provides, compared with the linear equalizer using the least mean squares (LMS) algorith, an interesting improvement concerning Bit Error Rate (BER) when channel distortions are nonlinear.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.34 no.5C
• /
• pp.521-526
• /
• 2009

In this paper, a complex-valued recursive least squares escalator filter algorithm with reduced computational complexity for complex-valued signal processing applications is presented. The local tap weight of RLS-ESC algorithm is updated by incrementing its old value by an amount equal to the local estimation error times the local gain scalar, and for the gain scalar, the local input autocorrelation is calculated at the previous time. By deriving a new gain scalar that can be calculated by using the current local input autocorrelation, reduced computational complexity is accomplished. Compared with the computational complexity of the complex-valued version of RLS-ESC algorithm, the computational complexity of the proposed method can be reduced by 50% without performance degradation. The reduced computational complexity of the proposed algorithm is even less than that of the LMS-ESC. Simulation results for complex channel equalization in 64QAM modulation schemes demonstrate that the proposed algorithm has superior convergence and constellation performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.6B
• /
• pp.1082-1091
• /
• 2000

In this paper, the efficient equalization method for OFDM(Orthogonal Frequency Division Multiflexing) System using the QAM(Quadrature Amplitude Modulation) in multipath fading channel is proposed in order to faster and more efficiently equalize the received signals that are sent over real channel. In generally, the one-tap linear equalizers have been used in the frequency-domain as the existing equalization method for OFDM system. In this technique, if characteristics of the channel are changed fast, the one-tap linear equalizers cannot compensate for the distortion due to time variant multipath channels. Therefore, in this paper, we use one-tap non-linear equalizers instead of using one-tap linear equalizers in the frequency-domain, and also use the linear equalizer in the time-domain to compensate the rapid performance reduction at the low SNR(Signal-to-Noise Ratio) that is the disadvantage of the non-linear equalizer. In the frequency-domain, when QAM signals, consisting of in-phase components and quadrature (out-phase) components, are sent over the complex channel, the only in-phase and quadrature components of signals distorted by the multipath fading are changed the same as signals distorted by the noise. So the cross components are canceled in the frequency-domain equalizer. The time-domain equalizer and the adaptive algorithm that has lower-error probability and fast convergence speed are applied to compensate for the error that is caused by canceling the cross components in the frequency-domain equalizer. In the time-domain, To compensate for the performance of frequency-domain equalizer the time-domain equalizes the distorted signals at a frame by using the Gold-code as a training sequence in the receiver after the Gold-codes are inserted into the guard signal in the transmitter. By using the proposed equalization method, we can achieve faster and more efficient equalization method that has the reduced computational complexity and improved performance.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.49 no.5
• /
• pp.269-277
• /
• 2000

We propose two new types multi-stage blind clustering equalizers for QAM demoulation, which are called a complex classification algorithm(CCA) and a radial basis function algorithm(RBFA). The CCA uses a clustering technique based on the joint gaussian probability function and computes separately the real part and imaginary part for simple implementation as well as less computation. In order to improve the performance of CCA, the Dual-Mode CCA(DMCCA) incorporates the CCA tap-updating mode with the decision-directed(DD) mode. The RBFA reduces the number of cluster centers through three steps using the classification technique of RBF and then updates the equalizer taps for QAM demodulation. Test results on 16-QAM confirm that the proposed algorithms perform better the conventional multi-state equalizers in the senses of SER and MSE under multi-path fading channel.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.12
• /
• pp.5036-5041
• /
• 2010

This paper introduces the complex-version of an Euclidean distance minimization algorithm based on a set of delta functions. The algorithm is analyzed to be able to compensate inherently the channel phase distortion caused by inferior complex channels. Also this algorithm has a relatively small size of Gaussian kernel compared to the conventional method of using a randomly generated symbol set. This characteristic implies that the information potential between desired symbol and output is higher so that the algorithm forces output more strongly to gather close to the desired symbol. Based on 16 QAM system and phase distorted complex-channel models, mean squared error (MSE) performance and concentration performance of output symbol points are evaluated. Simulation results show that the algorithm compensates channel phase distortion effectively in constellation performance and about 5 dB enhancement in steady state MSE performance.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.21 no.10
• /
• pp.2579-2585
• /
• 1996

In this paper, a complex fuzzy adaptive decision feedback equalizer(CFADFE) based on the LMS algorithm is proposed. The propoed equalizer is based on the complex fuzzy adaptive equalizer. The CFADFE isconstructed from a set of changeable complex fuzzy IF-THEN rules, where the 'IF' part of the rule is characterized by the state from a set of changealble complex fuzzy IF-THEN rules, where the 'IF' part of the rule is characterized by the state of the desision feedback. the role of decision feedback is to reduce the computational complexity. Computer simulation of the decision feedback. The role of decision feedback is to reduce the computational complexity. Computer simulation shosw that the CFADFE notonly reduces the computational complexity but also improves the performance compared with the conventional complex fuzzy adaptive equalizers. We also show that the adaptation speed is greatly improved by incorporating some linguistic information about the channel into the equalzer. It is applied to M-ary QAM digital communication system with linear and nonlinear complex channel characteristics.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.1A
• /
• pp.35-40
• /
• 2011

The complex-valued blind algorithm based on a set of randomly generated symbols and Euclidean distance can take advantage of information theoretic learning and cope with the channel phase rotation problems. On the algorithm, in this paper, the effect of kernel size has been studied and a kernel-modified version of the algorithm that rearranges the forces between the information potentials by kernel-modification has been proposed. In simulation results for 16 QAM and complex-channel models, the proposed algorithm show significantly enhanced performance of symbol-point concentration and no phase rotation problems caused by the complex channel models.

• Journal of the Korean Institute of Telematics and Electronics A
• /
• v.31A no.9
• /
• pp.18-25
• /
• 1994

Previously. TCM decoder has been using the Viterbi algorithm that was complex. In order to reduce the complexity of decoder. we proposed the “modified decoding algorithm using the path back method” and the modified decoding algorithm which extends the previous decoding algorithm to be able to applied to TCM decoder in this paper. On the gaussian and impulsive noise channel. Monte-Carlo simulation is used for analyzing the TCM performance and proving the efficiency of proposed decoding algorithm.

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