低复杂度的大规模MIMO分布式信道估计

针对大规模多输入多输出(multiple-input multiple-output, MIMO)系统传统信道矩阵获取方式导频开销大、计算复杂度高的问题，提出了一种低复杂度的二阶段分布式信道估计方案。该方案的初始阶段在基站侧采用传统压缩感知算法恢复信道矩阵，第2阶段在用户端利用信道的时间相关性，将大规模MIMO的角度域信道分解为密集部分和稀疏部分，并分别估计以实现连续信道追踪。稀疏部分信道通过所提的分布式自适应弱匹配追踪(distributed adaptive weak matching pursuit, DAWMP)算法，利用子信道的联合稀疏性进行多维重建。相比于线性最小均方误差(linear minimum mean square error, LMMSE)算法，所提方案的信道分解策略有效减少了在用户端进行信道估计的计算复杂度。仿真结果表明，所提算法与经典压缩感知信道估计算法相比，计算复杂度降低了约33%,算法性能提升了约0.5 dB。

Low complexity distributed channel estimation for massive MIMO

The traditional channel estimation algorithm in massive multiple-input multiple-output (MIMO) system requires a large pilot overhead and has high computation complexity. To solve this problem, we propose a two-stage distributed channel estimation scheme with low complexity. In the initial stage of the scheme, the traditional compressed sensing algorithm is used to recover the channel matrix at the base station side. In the second stage, the proposed scheme realizes continuous channel tracking by using the temporal correlation of the channel on the user side. The massive MIMO angle domain channel is divided into dense part and sparse part. The distributed adaptive weak matching pursuit (DAWMP) algorithm proposed in this paper is used for multi-dimensional reconstruction of sparse channel by utilizing the joint sparsity of sub-channels. Compared with the linear minimum mean square error (LMMSE) algorithm, the channel decomposition strategy effectively reduces the computational complexity of channel estimation on the user side. At the same time, simulation results show that compared with the classical compressed sensing channel estimation algorithm, the computational complexity of the proposed algorithm is reduced by about 33%, and the performance of the algorithm is improved by about 0.5 dB.