高效的基于数据与模型的信道估计算法

Efficient model-and-data based channel estimation algorithm

针对正交频分复用(OFDM)系统,提出一种新型的数据与模型联合驱动下的信道估计算法。该算法结合一种可在线训练的低复杂度学习型估计方法与线性最小均方误差(LMMSE)估计,既赋予信道估计器通过在线训练提升了估计性能的能力,又借助模型解决了在线生成训练数据会造成额外导频开销的问题,提升了系统效率。仿真结果表明,所提算法在低信噪比下的性能和对实际非理想因素的适应性等方面优于传统信道估计算法。

For orthogonal frequency division multiplexing(OFDM)systems,a hybrid model and data driven channel estimation algorithm was proposed.Combined with two existing channel estimation methods,including a low complex learning-based channel estimation method and the linear minimum mean square error(LMMSE)channel estimation,the estimator with the ability was facilitated to employ online training to improve estimation performance.Meanwhile,the pilot overhead consumed by generating online training data was saved due to the use of the model-based method in the proposed algorithm,which improved the spectrum efficiency.The simulation results demonstrate that the proposed algorithm has better performance under low signal-to-noise ratio(SNR)and better adaptation to practical imperfections compared with conventional channel estimation methods.