In this paper, the problem of blind equalization of high-order quadrature amplitude modulation (QAM) signals is tackled by using a batch equalizer based on support vector regression (SVR). A new set of error functions...
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In this paper, the problem of blind equalization of high-order quadrature amplitude modulation (QAM) signals is tackled by using a batch equalizer based on support vector regression (SVR). A new set of error functions weighted by neighborhood symbol decisions and augmented by generalized power factors p and q, are proposed to be used as the penalty terms in SVR, and the optimal values of p and q are determined. In addition, we propose a method to remove the high online computational complexity incurred by the inclusion of neighborhood terms in the new error function. Simulation results show that with about the same complexity, the optimized SVR-NA-SBD-(p,q) attain much lower residual inter-symbol-interference and higher probability of convergence than the best known SVR-MMA, and it needs only about 1400 symbols to achieve a BER of 10(-4) for 256QAM in a multipath channel. In contrast, the conventional SVR-MMA needs more than 4000 symbols to achieve such BER.
In this paper, two families of batch algorithms are constructed in the support vector regression (SVR) framework for blind equalization of multilevel signals. Specifically, the error functions of constant modulus algo...
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ISBN:
(纸本)9781538643624
In this paper, two families of batch algorithms are constructed in the support vector regression (SVR) framework for blind equalization of multilevel signals. Specifically, the error functions of constant modulus algorithm CMA(p, 2) and multimodulus algorithm MMA(p,2) are contained in the penalty term of the SVR. Simulation results show that the proposed MMA(p,2)-based algorithms perform better than the CMA(p,2)based ones, which exhibit lower residual intersymbol interference (ISI) and higher probability of convergence. With respect to conventional dual-mode scheme, the MMA(p,2)-based algorithms show better performance in the case of higher noise or smaller data block, therefore they are robust and more suitable for multilevel signals. In addition, they avoid tedious switching mechanism of dual-mode scheme and overcome phase rotation.
Polarization-division multiplexing (PDM) has emerged as a promising technique for increasing data rates without increasing symbol rates. However, the distortion effects of the fiber transmission medium poses severe ba...
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ISBN:
(纸本)9781479975051
Polarization-division multiplexing (PDM) has emerged as a promising technique for increasing data rates without increasing symbol rates. However, the distortion effects of the fiber transmission medium poses severe barriers for the implementation of this technological alternative. Especially, due to the fiber-induced polarization fluctuation orthogonally transmitted PDM signals are mixed at the receiver input. Therefore, a receiver compensation structure needs to be implemented to recover the original orthogonal transmitted components from their mixtures at the end of the fiber channel. This is in fact the focus of this article where a receiver algorithm is based on a recently proposed minimum entropy equalization scheme exploiting the maximization of (an enhanced) energy cost function subject to the magnitude boundedness of (incoming) digital communication signals. Through the use of this scheme, new receiver algorithms for recovering the original polarization signals in an adaptive manner are proposed. The key feature of these algorithms is that they can achieve high equalization performance while maintaining the algorithmic complexity in a fairly low level that is suitable for implementation in optical fiber communication receivers. The performance of these algorithms for a square-QAM based coherent polarization division multiplexed system are illustrated through some simulation examples.
For improving the equalization performance of higher-order QAM signals, a generalized multi-modulus blind equalization algorithm based on chaos artificial fish swarm optimization is proposed. In this proposed algorith...
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For improving the equalization performance of higher-order QAM signals, a generalized multi-modulus blind equalization algorithm based on chaos artificial fish swarm optimization is proposed. In this proposed algorithm, according to the prior information of higher-order QAM signal constellations, chaotic artificial fish swarm algorithm is fused to generalized multi-modulus blind equalization algorithm. Accordingly, the proposed algorithm uses rapid global optimum searching ability of chaotic artificial fish swarm algorithm(CAFSA) to initialize equalizer weight vector and adjusts adaptively modulus value of objective function in the equalizer vector iterations. The theoretical analyses and computer simulations indicate that the proposed algorithm outperforms generalized multimodulus algorithm(GMMA) and artificial fish swarm algorithm based GMMA(AFSA-GMMA) in mean square error and convergence rate, which is more efficient for high-order QAM signals.
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