归一化加权双模式盲均衡算法

恒模算法（CMA）打开信道眼图的能力强，但收敛速度慢，稳态误差大，适于均衡器启动阶段的工作；决策指向算法（DD）收敛速度快，稳态误差低，但要求决策装置正确判决率高，适于跟踪阶段的工作；归一化自适应滤波算法能够动态地调整步长因子，提高收敛速度。结合上述三方面的优势，提出一种归一化的加权双模式盲均衡算法，和切换式双模式算法相比，新算法的主要优势在于无需在两种模式之间切换，降低了接收设备的复杂性。针对16QAM系统的仿真结果表明新算法和切换式算法相比，稳健性更好，收敛速度更快，并且更适合在低信噪比条件下使用。

Normalized weighted dual-mode blind equalization algorithm

Constant modulus algorithm （CMA） is robust in opening the channel eye, making it suited for start-up phase. But the convergence rate is slow and the steady state error is high. Decision directed algorithm has fast convergence rate and low steady state error, but demands high correctness of threshold device, making it suited for trace phase. By virtue of adjusting the step size dynamically, normalized adaptive filtering algorithm can improve the convergence rate. Combining the advantages mentioned above, a weighted dual-mode blind equalization algorithm for 16QAM system is proposed in this paper. Comparing to switched dual-mode algorithm, the equalizer is more robust and the convergence rate is faster. What＇s more, the new algorithm reduces the complexity of the receivers by avoiding switching over between two different modes. And it performs better in low signal to noise ratio circumstances.