基于负熵最大化的压缩感知信道估计算法

Channel estimation algorithm based on compressed sensing with maximizing negative entropy

讨论了在5G时代随着移动终端和网络流量的增长、大规模机器类型通信的产生以及天基卫星网络与地面蜂窝主干网络的结合,使得移动无线通信的频谱使用范围更宽,导致信道具有更为明显的稀疏性,从而得以利用无线信道的稀疏性引入压缩感知技术来进行无线通信系统的信道估计。通过负熵最大化算法与l_(p)正则化改进现有的压缩感知信道估计算法,将传统的最小化误差函数均方误差替代为最大化目标函数的负熵以适应信道非高斯噪声,同时稀疏约束采用更为精确的l_(p)正则化以加强信道估计算法的稀疏表示。研究表明,该算法不仅可以提高信道估计精度,增强抗噪声鲁棒性;另一方面可以利用更少的导频实现更高精度的信道估计,具备提高系统频谱利用率的作用。

In the 5G era,with the growth of mobile devices and network traffic,the generation of large-scale machine-type communications,and the combination of space-based satellite networks and terrestrial cellular backbone networks,mobile wireless communication system uses broader frequency spectrum.It results in a more visible sparsity of the channel,which enables the compressed sensing technology to be used in wireless communication.The existing compressed sensing channel estimation algorithm is improved by the negative entropy maximization algorithm and l_(p)regularization.The traditional mean square error of the minimization error function is replaced by the negative entropy of the maximization objective function to accommodate the non-Gaussian noise of the channel.Sparse constraints use more precise l_(p)regularization to enhance the sparse representation of channel estimation algorithms.Research shows that the algorithm can not only improve the accuracy of channel estimation,but also enhance the robustness against noise.On the other hand,fewer pilots can be used to achieve more accurate channel estimation,which has the effect of improving system spectrum utilization.