基于LASSO算法的波束空间信道估计

Beam space channel estimation based on LASSO algorithm

在大规模毫米波(mmWave)天线阵列通信中,多输入多输出(Multiple Input Multiple Output,MIMO)系统可以使用透镜天线阵列大幅减少射频链的数量,但由于天线的数量远远大于射频链路的数量,信道估计具有挑战性。由于波束空间信道具有稀疏的特性,那么用于求解稀疏信号恢复问题的算法,可以作为波束空间信道估计问题的解决方法。波束空间信道估计问题建立的模型是基于l0-范数的非凸性问题,该问题为NP-hard。通常用l1-范数代替l0-范数,将该问题转化为凸优化问题。该凸优化问题可以用传统的贪心算法方法进行求解。然而,这些贪心算法估计精度差。而且随着稀疏度的增加,计算复杂度也会增加。文章提出了最小角回归(Least Angle Regression,LARS)算法和改进的最小绝对收缩和选择算子(Least Absolute Shrinkage and Selection Operator,LASSO)算法,高效地解决了稀疏信号的恢复问题,即波束信道的估计。实验仿真结果表明,LARS算法和所提出的改进LASSO算法能够获得比传统OMP算法更高效且具有更好的波束空间信道估计精度。

In large-scale millimeter wave(mmWave)antenna array communication,Multiple Input Multiple Output(MIMO)systems can use lens antenna arrays to significantly reduce the number of RF chains,but channel estimation is challenging because the number of antennas is much larger than the number of RF links.Since the beam-space channel is sparse,the algorithm used to solve the problem of sparse signal recovery can be used as a solution to the problem of beam-space channel estimation.The model established for this problem is a non-convexity problem based on l0-norm,which is NP-hard.The l0-norm is usually replaced by l1-norm to transform the non-convexity into a convex optimization problem.The convex optimization problem can be solved by the traditional greedy algorithm.However,these greedy algorithms have poor estimation accuracy.Moreover,with the increase of sparsity,the computational complexity will also increase.In this paper,an algorithm for Least Angle Regression(LARS)and an improved Least Absolute Shrinkage and Selection Operator(LASSO),the LASSO algorithm is efficient to solve the sparse signal recovery,that is,the estimation of the beam channel.Experimental simulation results show that LARS algorithm and the proposed improved LASSO algorithm can obtain more efficient and better accuracy of beam space channel estimation than the traditional OMP algorithm.