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.

中文翻译：

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数据长度短的高阶QAM的支持向量机盲均衡

本文通过基于支持向量回归（SVR）的批处理均衡器解决了高阶正交调幅（QAM）信号的盲均衡问题。一组新的误差函数，由邻域符号决策加权，并由广义功率因数增强$ p $ 和 $ q $ 建议用作SVR中的惩罚项，并且的最佳值 $ p $ 和 $ q $确定。此外，我们提出了一种方法来消除由于在新的误差函数中包含邻域项而导致的高在线计算复杂度。仿真结果表明，优化后的SVR-NA-SBD- $（p，q）$ 与最知名的SVR-MMA相比，可以获得更少的残留符号间干扰和更高的收敛概率，并且仅需要大约1400个符号即可达到BER $ 10 ^ {-4} $在多径通道中为256QAM。相反，常规的SVR-MMA需要超过4000个符号才能实现这样的BER。