MC-CDMA信号的类型识别及参数盲估计

摘要

The modified cyclic autocorrelation algorithm is proposed to estimate problems of the useful data period, entire symbol period, chip duration, and guard interval length in MC-CDMA signal. Firstly, the autocorrelation function of the received MC-CDMA signal is computed. Then, Fourier transformation and accumulation in frequency-domain are used. Finally, through detecting the interval of the peak pulses in different slices, parameters mentioned above can be estimated. In addition, a new method of estimating symbol period, the accumulative average method, is developed. By averaging amplitudes of the spectral lines in each column parallel to the delay axis, the symbol duration can be obtained. Cyclic autocorrelation expression of the MC-CDMA signal is derived theoretically, and it is proved that MC-CDMA signal with cyclic prefix has cyclostationary. The simulation results show that, the improved cyclic autocorrelation algorithm is effective in the low signal-to-noise cause.%针对MC-CDMA信号符号周期、有用数据周期、码片持续时间以及保护间隔长度等参数盲估计问题,该文提出一种改进型的循环自相关算法.首先在接收端对MC-CDMA信号求自相关函数,取傅氏变换,再在频域累积,通过检测不同切片上的谱峰间隔,可以同时估计以上多个参数.此外,根据MC-CDMA信号自身特点,提出一种新的估计符号周期方法—累加平均法,将3维图中平行于延时轴的每列谱线累加平均(间隔为符号周期),可估计出符号周期.理论推导了MC-CDMA信号的循环自相关表达式,证明加循环前缀的MC-CDMA信号具有循环平稳性.仿真实验证明了改进型的频域累积循环自相关算法的有效性,且适用于低信噪比环境.