基于部分数据的叠加序列慢时变信道估计

针对OFDMA通信系统,提出了一种基于部分数据的叠加序列慢时变信道估计算法,并在接收端给出了数据恢复的方法。时变信道采用复指数基扩展模型来描述,对OFDMA系统的导频序列进行了精心设计。提出在频域减去一个基于部分数据的序列,从而使发送数据经过反傅里叶变换后,特定位上的数据能量变低,进而大大减少了数据在信道估计时产生的误差。由于在发射端信号已经产生了畸变,在接收端采用特定的方法对数据进行补偿,消除了这种影响。实验仿真证明该方法在一定程度上提高信道估计的性能。

Slow Time-varying Channel Estimation Based on Part-data-dependent Superimposed Training

This paper researches the OFDMA communication system,and proposes a new channel estimation algorithm using part-data-dependent superimposed training(PDDST).Recovery method of data at the receiver is given.Time-varying channel is modeled by complex-exponential basis expansion model(CE-BEM).The pilots of the OFDMA system have been designed carefully.In the frequency domain,the data is to be launched minus a sequence based on part of the data.After the Fourier transform,a part of data which will be sent has lower power in time domain,and errors produced by Channel estimation are greatly reduced.Since the signal launched has been distorted,we propose compensation for information data to avoid reducing the performance of data recovery.Simulations prove that this method can significantly improve the performance of channel estimation.