[author_cn_name].[cn_title][J].空军工程大学学报:自然科学版,[year_id],[volume]([issue]):[start_page]-[end_page] 基于余弦代价函数的双模盲均衡算法-A Dual mode Blind Equalization Algorithm Based on Cosine Cost Function
文章摘要
王旭光,陈红,褚鼎立.基于余弦代价函数的双模盲均衡算法[J].空军工程大学学报:自然科学版,2019,20(4):78-83
基于余弦代价函数的双模盲均衡算法
A Dual mode Blind Equalization Algorithm Based on Cosine Cost Function
  
DOI:
中文关键词: 盲均衡  脉冲噪声  凸组合结构  余弦代价函数
英文关键词: blind equalization  impulse noise  convex combination  cosine cost function
基金项目:
作者单位
王旭光,陈红,褚鼎立 国防科技大学电子对抗学院,合肥,230000 
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中文摘要:
      盲均衡算法不需要训练序列,就能够有效降低码间串扰(ISI),但是在脉冲噪声环境下,现有单滤波器均衡算法不能有效平衡收敛速率与均衡误差,算法收敛后ISI仍然较高。针对上述问题,提出了一种基于余弦代价函数的凸组合双模盲均衡算法。该算法将2个盲均衡器并联使用,其中一个作为快速滤波器以保证收敛速率,另一个作为慢速滤波器以降低均衡误差。为了进一步抑制脉冲噪声,将分数低阶统计量引入到基于余弦代价函数的盲均衡算法和基于判决反馈准则的盲均衡算法中,并分别作为快速滤波器和慢速滤波器的权向量更新算法。仿真实验表明:当噪声设置为25 dB的高斯白噪声时,新算法收敛后ISI会低于常模盲均衡算法CMA和基于余弦代价函数的盲均衡算法CCF,星座图也较为清晰;当噪声环境为28 dB的α稳定分布噪声时,新算法利用分数低阶统计量以抑制脉冲噪声,能够得到较低的ISI和清晰的星座图,而凸组合结构兼顾了稳态误差与收敛速率,在进一步降低稳态误差的同时确保了较快的收敛速率。
英文摘要:
      Aimed at the problems that the blind equalization algorithm does not require a sequence and can effectively reduce inter symbol interference, but under the impulse noise environment, the existing single filter equalization algorithm fails to effectively balance the convergence rate and steady state error, and fails to effectively reduce ISI, a convex combination blind equalization algorithm is proposed based on cosine cost function. The algorithm utilizes two blind equalizers in parallel ( one as a fast filter ) for guaranteeing the convergence rate and ( the other as a slow filter) for reducing the equalization error. In order to further reduce the impact of impulse noise, the fractional low order statistic is introduced into the blind equalization algorithm based on the cosine cost function and the blind equalization algorithm based on the decision detection algorithm. These two algorithms are used as weight vector update algorithms for fast and slow filters, respectively. The simulation results show that when the noise is set to Gaussian white noise of 28 dB, the ISI of the new algorithm will be lower than CMA and CCF and the constellation diagram is also clear. When the noise environment is 28 dB α stable distribution noise, the new algorithm uses the fractional low order statistic to suppress the impulse noise to obtain lower ISI and clear constellation, and the convex combination structure takes into account the steady state error and convergence rate, further reducing the steady state error while ensuring a faster convergence rate.
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