用于非方形均匀平面阵列辅助的超大规模多输入多输出（XL-MIMO）系统的低复杂度信道估计框架
Low-Complexity Channel Estimation Framework for Non-Square UPA-Assisted XL-MIMO Systems
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中文总结 AI 辅助
针对非方形UPA辅助的XL-MIMO系统在混合场环境中面临的信道估计难题，提出低复杂度框架。通过天线域和相关域外推方案及算法解耦参数，突破分辨率限制，验证了该框架能有效提升信道估计性能。
中文摘要 AI 辅助
低复杂度信道状态信息获取对于超大规模多输入多输出（XL-MIMO）系统至关重要。然而，在混合场环境中实际部署非方形均匀平面阵列（UPA）时，由于仰角到达角（AoA）分辨率有限和信道稀疏性恶化，面临着极高的计算复杂度和降低的估计精度。为应对这些挑战，我们提出了一个低复杂度信道估计框架。首先，天线域外推方案通过相邻元素间的空间相关性合成虚拟扩大的垂直孔径，突破仰角分辨率限制。该框架接着通过将二维联合搜索转换为两个顺序的一维搜索来解开参数耦合。具体而言，沿着虚拟扩大的垂直均匀线性阵列（ULA），通过外推增强离散傅里叶变换 - 牛顿化正交匹配追踪（NOMP）算法提取仰角AoA，而沿着水平ULA利用离散分数傅里叶变换 - NOMP算法获取方位角AoA、距离和增益。子空间拟合驱动的路径匹配算法将这些解耦的参数配对。为克服天线域方案的精度瓶颈，通过利用空间相关矩阵的结构特性进一步开发相关域外推方案，以解耦近场二次和方位相位分量，产生噪声抑制的虚拟阵列。数值结果验证了所提出框架的有效性。
英文摘要
Low-complexity channel state information acquisition is crucial for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. However, practical deployments of non-square uniform planar arrays (UPAs) in hybrid-field environments face prohibitive computational complexity and degraded estimation accuracy due to limited elevation angle-of-arrival (AoA) resolution and deteriorated channel sparsity. To tackle these challenges, we propose a low-complexity channel estimation framework. First, an antenna-domain extrapolation scheme synthesizes a virtually enlarged vertical aperture via the spatial correlation among adjacent elements, breaking the elevation resolution limit. The framework then disentangles the parameter coupling by transforming the two-dimensional joint search into two sequential one-dimensional searches. Specifically, elevation AoAs are extracted via an extrapolation-enhanced discrete Fourier transform-Newtonized orthogonal matching pursuit (NOMP) algorithm along the virtually enlarged vertical uniform linear array (ULA), while azimuth AoAs, ranges, and gains are acquired utilizing a discrete fractional Fourier transform-NOMP algorithm along a horizontal ULA. A subspace fitting-driven path matching algorithm pairs these decoupled parameters. To overcome the accuracy bottleneck of the antenna-domain scheme, a correlation-domain extrapolation scheme is further developed by exploiting the structural properties of the spatial correlation matrix to decouple the near-field quadratic and azimuth phase components, yielding a noise-suppressed virtual array. Numerical results validate the effectiveness of the proposed framework.
