基于均匀线性阵列的超大规模MIMO混合场信道估计算法

Hybrid field channel estimation algorithm for extremely large-scale MIMObased on uniform linear array

超大规模MIMO(extremely large-scale massive MIMO,XL-MIMO)是未来6G通信的关键技术之一。现有的XL-MIMO混合场信道模型大多对均匀线性阵列和单天线用户之间信道建模,且采用散射体最后一跳模型。若收发双端均配备线性阵列,现有的混合场信道估计方案将不再适用。为此,针对收发端均部署超大规模线性阵列的XL-MIMO场景,采用Saleh-Valenzuela模型进行信道建模,并提出了一种基于正交匹配追踪(OMP)的混合场信道估计算法。该算法首先利用角域变换矩阵对远场分量进行估计,然后通过极域变换矩阵估计近场分量。此外,引入克拉美罗-下界(CRLB)对所提算法进行评估。仿真结果表明,提出的混合场估计算法相较于仅考虑远场和近场的估计算法在信道估计性能上有约0.6 dB的提升。

Extremely large-scale massive MIMO(XL-MIMO)emerges as a crucial technology for future 6G communication.Existing XL-MIMO hybrid-field channel models primarily model channels between uniform linear arrays and single-antenna users,employing the last-hop model of scatterers.However,if both transmitter and receiver are equipped with linear arrays,existing hybrid-field channel estimation schemes become inadequate.To address this,this paper proposed a channel modeling approach for XL-MIMO scenarios with both transmitter and receiver equipped with large-scale linear arrays,utilizing the Saleh-Valenzuela model.Additionally,it introduced a hybrid-field channel estimation algorithm based on orthogonal matching pursuit(OMP).This algorithm initially estimated far-field components using the angle-domain transformation matrix,followed by estimating near-field components using the polar-domain transformation matrix.Furthermore,it introduced the Cramér-Rao lower bound(CRLB)to evaluate the proposed algorithm.Simulation results demonstrate approximately 0.6 dB improvement in channel estimation performance compared to algorithms considering only far-field and near-field components.